+ 
-
+
MIT C++ MLIAPMIT C++ MLIAPCC-BY-4.0 general-ml PythonLM generative pre-trained
+
+🔗 matsci.org - A community forum for the discussion of anything materials science, with a focus on computational materials science..
+
+🔗 Matter Modeling Stack Exchange - Machine Learning - Forum StackExchange, site Matter Modeling, ML-tagged questions.
+
+CC-BY-4.0 general-ml PythonMIT general-ml rep-learnMIT general-ml rep-learnCustom t o p i c s / m a t e r i a l s - i n f o r m a t i c sMIT datasets benchmarkingGPL-3.0 licenseCustomUnlicensed datasetsUnlicensed datasetsMIT
-- GitHub topic materials-informatics (➕) - Unlicensed
+- GitHub topic materials-informatics - Unlicensed
+- MateriApps (➕) - Unlicensed
material-defect
+
🔗 Catalysis Hub - A web-platform for sharing data and software for computational catalysis research!.
+🔗 Citrination Datasets
+
🔗 crystals.ai - Curated datasets for reproducible AI in materials science.
🔗 DeepChem Models pre-trained LM
🔗 JARVIS-Leaderboard ( ⭐ 39) - This project provides benchmark-performances for materials science applications including Artificial Intelligence.. benchmarking
+🔗 Materials Project - Charge Densities
+
🔗 matterverse.ai - Database of yet-to-be-sythesized materials predicted using state-of-the-art machine learning algorithms.
-🔗 Quantum-Machine.org Datasets - Collection of datasets, including QM7, QM7b, QM9, etc. MD and DFT data of various systems. Mostly small organic..
+🔗 Quantum-Machine.org Datasets - Collection of datasets, including QM7, QM9, etc. MD, DFT. Small organic molecules, mostly.
🔗 sGDML Datasets
+MITCC-BY-4.0MIT MLIAP MDMITMITMITLGPL-2.1 knowledge-base pre-trained
+- OpenKIM (🥈11 · ⭐ 29 · 💀) - The Open Knowledgebase of Interatomic Models (OpenKIM) aims to be an online resource for standardized testing, long-.. LGPL-2.1 knowledge-base pre-trained
- MoleculeNet Leaderboard (🥈8 · ⭐ 73 · 💀) - MIT benchmarking
-- ANI-1x Datasets (🥈6 · ⭐ 45 · 💀) - The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules. MIT
-- COMP6 Benchmark dataset (🥈6 · ⭐ 35 · 💀) - COMP6 Benchmark dataset for ML potentials. MIT
-- GEOM (🥉5 · ⭐ 130 · 💀) - GEOM: Energy-annotated molecular conformations. Unlicensed drug-discovery
+- ANI-1x Datasets (🥉6 · ⭐ 45 · 💀) - The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules. MIT
+- COMP6 Benchmark dataset (🥉6 · ⭐ 35 · 💀) - COMP6 Benchmark dataset for ML potentials. MIT
+- GEOM (🥉5 · ⭐ 130 · 💀) - GEOM: Energy-annotated molecular conformations. Unlicensed drug-discovery
- linear-regression-benchmarks (🥉5 · ⭐ 1 · 💀) - Data sets used for linear regression benchmarks. MIT benchmarking single-paper
BSD-3BSD-3
+- PROPhet (🥈9 · ⭐ 59 · 💀) - PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches. GPL-3.0 MLIAP MD single-paper C++
- Libnxc (🥈7 · ⭐ 13 · 💀) - A library for using machine-learned exchange-correlation functionals for density-functional theory. MPL-2.0 C++ Fortran
- charge-density-models (🥉4 · ⭐ 2) - Tools to build charge density models using ocpmodels. MIT
- gprep (🥉4 · 💀) - Fitting DFTB repulsive potentials with GPR. MIT single-paper
- CSNN (🥉4 · 💤) - Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning. BSD-3
- ML-DFT (🥉3 · ⭐ 18 · 💀) - A package for density functional approximation using machine learning. MIT
+- DeepCDP (🥉3 · ⭐ 2 · ➕) - DeepCDP: Deep learning Charge Density Prediction. Unlicensed
- MALADA (🥉3) - MALA Data Acquisition: Helpful tools to build data for MALA. BSD-3
- xDeepH (🥉2 · ⭐ 16 · 🐣) - Extended DeepH (xDeepH) method for magnetic materials. LGPL-3.0 magnetism Julia
- kdft (🥉1 · ⭐ 2 · 💀) - The Kernel Density Functional (KDF) code allows generating ML based DFT functionals. Unlicensed
@@ -368,7 +445,7 @@ _Tutorials, guides, cookbooks, recipes, etc._
git clone https://github.com/rdkit/rdkit-tutorials
```
Apache-2Apache-2MIT
+- MEGAN: Multi Explanation Graph Attention Student (🥈6 · ⭐ 1) - Minimal implementation of graph attention student model architecture. MIT
+- MEGAN (🥈6 · ⭐ 1 · ➕) - Minimal implementation of graph attention student model architecture. MIT XAI rep-learn
+- Linear vs blackbox (🥉4 · 💤) - Code and data related to the publication: Interpretable models for extrapolation in scientific machine learning. MIT XAI single-paper rep-eng
GPL-3.0 license rep-learn generative MLIAP MD ML-DFT ML-WFT biomoleculesGPL-3.0MITMIT pre-trained drug-discovery rep-learnMITMIT
- MolSLEPA (🥉4 · ⭐ 3 · 🐣) - Interpretable Fragment-based Molecule Design with Self-learning Entropic Population Annealing. MIT XAI
MIT rep-learnMITMIT drug-discovery recommenderMIT drug-discovery recommenderCustom MD pre-trained electrostatics magnetism structure-relaxationMITMITMITMIT MD C++MITGPL-3.0Unlicensed workflows HTCMITGPL-3.0 single-paper
- ANI-1 (🥈11 · ⭐ 200 · 💀) - ANI-1 neural net potential with python interface (ASE). MIT
- SIMPLE-NN (🥈11 · ⭐ 41 · 💀) - SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network). GPL-3.0
- ACEfit (🥈11 · ⭐ 4) - MIT Julia
@@ -1099,13 +1240,30 @@ _Projects that implement materials discovery methods using atomistic ML._
git clone https://github.com/CompRhys/aviary
```
MIT materials-discovery active-learning single-paperMIT materials-discovery active-learning single-paperUnlicensed probabilistic
- Computational Autonomy for Materials Discovery (CAMD) (🥈6 · ⭐ 1 · 💤) - Agent-based sequential learning software for materials discovery. Apache-2
-- AGOX (🥉5 · ⭐ 10 · 💀) - AGOX is a package for global optimization of atomic system using e.g. the energy calculated from density functional.. GPL-3.0 structure-optimization
+- AGOX (🥈5 · ⭐ 10 · 💀) - AGOX is a package for global optimization of atomic system using e.g. the energy calculated from density functional.. GPL-3.0 structure-optimization
- CSPML (crystal structure prediction with machine learning-based element substitution) (🥉3 · ⭐ 12 · 💀) - Original implementation of CSPML. Unlicensed structure-prediction
- SPINNER (🥉3 · ⭐ 9 · 💀) - SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random.. GPL-3.0 C++ structure-prediction
+- sl_discovery (🥉3 · ⭐ 5 · 💤) - Data processing and models related to Quantifying the performance of machine learning models in materials discovery. Apache-2 materials-discovery single-paper
MIT Julia
- cnine (🥉4 · ⭐ 1) - Cnine tensor library. Unlicensed C++
- Wigner Kernels (🥉4) - Collection of programs to benchmark Wigner kernels. Unlicensed benchmarking
Custom MLIAP C++MIT MLIAPCustomMIT drug-discovery
+- ReLeaSE (🥇11 · ⭐ 310 · 💀) - Deep Reinforcement Learning for de-novo Drug Design. MIT drug-discovery
- CatGym (🥉6 · ⭐ 10 · 💀) - Surface segregation using Deep Reinforcement Learning. GPL
LGPL-2.1 cheminformatics JavaApache-2GPL-3.0LGPL-2.1Apache-2 benchmarkingApache-2 benchmarkingMIT single-paper magnetismMIT benchmarking
-- CBFV (🥈7 · ⭐ 13 · 💀) - Tool to quickly create a composition-based feature vector. Unlicensed
-- pyLODE (🥈7 · ⭐ 2) - Pythonic implementation of LOng Distance Equivariants. Apache-2 electrostatics
+- SkipAtom (🥈8 · ⭐ 21 · 💀) - Distributed representations of atoms, inspired by the Skip-gram model. MIT
+- CBFV (🥉7 · ⭐ 13 · 💀) - Tool to quickly create a composition-based feature vector. Unlicensed
+- pyLODE (🥉7 · ⭐ 2) - Pythonic implementation of LOng Distance Equivariants. Apache-2 electrostatics
- fplib (🥉6 · ⭐ 7 · 💀) - a fingerprint library. MIT TmpClang single-paper
- SOAPxx (🥉6 · ⭐ 7 · 💀) - A SOAP implementation. GPL-2.0 C++
- magnetism-prediction (🥉4 · ⭐ 1) - DFT-aided Machine Learning Search for Magnetism in Fe-based Bimetallic Chalcogenides. Apache-2 magnetism single-paper
@@ -1418,7 +1638,7 @@ _General models that learn a representations aka embeddings of atomistic systems
pip install dgllife
```
Custom educational drug-discoveryCustom educational drug-discoveryMIT pre-trainedMIT pre-trainedCustomMITCustomMIT surface-science single-paperMIT optical-properties single-paper probabilisticMIT optical-properties single-paper probabilisticMITMITMIT transport-phenomenaMIT probabilistic pre-trained single-paperMIT single-paper benchmarking
- Crystal Graph Convolutional Neural Networks (CGCNN) (🥈12 · ⭐ 500 · 💀) - Crystal graph convolutional neural networks for predicting material properties. MIT
- Neural fingerprint (nfp) (🥈12 · ⭐ 53 · 💀) - Keras layers for end-to-end learning with rdkit and pymatgen. Custom
- SE(3)-Transformers (🥈10 · ⭐ 410 · 💀) - code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503. MIT single-paper
+- ai4material_design (🥈10 · ⭐ 1 · ➕) - Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of.. Apache-2 pre-trained material-defect
- molecularGNN_smiles (🥉9 · ⭐ 250 · 💀) - The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius.. Apache-2
- DTNN (🥉7 · ⭐ 77 · 💀) - Deep Tensor Neural Network. MIT
- Cormorant (🥉7 · ⭐ 53 · 💀) - Codebase for Cormorant Neural Networks. Custom
+- tensorfieldnetworks (🥉5 · ⭐ 140 · 💀) - MIT
- Autobahn (🥉5 · ⭐ 26 · 💀) - Repository for Autobahn: Automorphism Based Graph Neural Networks. MIT
- charge_transfer_nnp (🥉5 · ⭐ 20 · 💀) - Graph neural network potential with charge transfer. MIT electrostatics
- SCFNN (🥉5 · ⭐ 14 · 💀) - Self-consistent determination of long-range electrostatics in neural network potentials. MIT C++ electrostatics single-paper
- FieldSchNet (🥉5 · ⭐ 9 · 💀) - MIT
- Per-Site CGCNN (🥉5 · ⭐ 1 · 🐣) - Crystal graph convolutional neural networks for predicting material properties. MIT pre-trained single-paper
- Graph Transport Network (🥉4 · ⭐ 14) - Graph transport network (GTN), as proposed in Scalable Optimal Transport in High Dimensions for Graph Distances,.. Custom transport-phenomena
+- Atom2Vec (🥉3 · ⭐ 24 · 💀) - Atom2Vec: a simple way to describe atoms for machine learning. Unlicensed
+- Element encoder (🥉3 · ⭐ 5 · 💀) - Autoencoder neural network to compress properties of atomic species into a vector representation. GPL-3.0 single-paper
- atom_by_atom (🥉3 · ⭐ 2 · 🐣) - Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with Machine Learning. Unlicensed surface-science single-paper
- Point Edge Transformer (🥉2) - Smooth, exact rotational symmetrization for deep learning on point clouds. CC-BY-4.0
- gkx: Green-Kubo Method in JAX (🥉1 · ⭐ 2 · 🐣) - Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast. MIT transport-phenomena
diff --git a/history/2023-08-25_changes.md b/history/2023-08-25_changes.md
index e271399..0589a42 100644
--- a/history/2023-08-25_changes.md
+++ b/history/2023-08-25_changes.md
@@ -1,27 +1,43 @@
-## 📈 Trending Up
-
-_Projects that have a higher project-quality score compared to the last update. There might be a variety of reasons, such as increased downloads or code activity._
-
-- FLARE (🥇19 · ⭐ 240 · 📈) - An open-source Python package for creating fast and accurate interatomic potentials. MIT C++ MLIAP
-- Uni-Mol (🥈17 · ⭐ 400 · 📈) - Official Repository for the Uni-Mol Series Methods. MIT pre-trained
-- PyXtalFF (🥈15 · ⭐ 71 · 📈) - Machine Learning Interatomic Potential Predictions. MIT
-- UVVisML (🥉8 · ⭐ 11 · 📈) - Predict optical properties of molecules with machine learning. MIT optical-properties single-paper probabilistic
-- BOSS (🥈7 · ⭐ 18 · 💀) - Bayesian Optimization Structure Search (BOSS). Unlicensed probabilistic
-
-## 📉 Trending Down
-
-_Projects that have a lower project-quality score compared to the last update. There might be a variety of reasons such as decreased downloads or code activity._
-
-- iam-notebooks (🥈8 · ⭐ 16 · 📉) - Jupyter notebooks for the lectures of the Introduction to Atomistic Modeling. Apache-2
-
## ➕ Added Projects
_Projects that were recently added to this best-of list._
-- Best-of Machine Learning with Python (🥇22 · ⭐ 14K · ➕) - A ranked list of awesome machine learning Python libraries. Updated weekly. CC-BY-4.0 general-ml Python
-- Graph-based Deep Learning Literature (🥈18 · ⭐ 4.3K · ➕) - links to conference publications in graph-based deep learning. MIT general-ml rep-learn
-- Awesome Materials Informatics (🥉11 · ⭐ 290 · ➕) - Curated list of known efforts in materials informatics = modern materials science. Custom t o p i c s / m a t e r i a l s - i n f o r m a t i c s
-- The Collection of Database and Dataset Resources in Materials Science (🥉8 · ⭐ 160 · ➕) - A list of databases, datasets and books/handbooks where you can find materials properties for machine learning.. Unlicensed datasets
-- A Highly Opinionated List of Open-Source Materials Informatics Resources (🥉7 · ⭐ 93 · 💀) - A Highly Opinionated List of Open Source Materials Informatics Resources. MIT
-- GitHub topic materials-informatics (➕) - Unlicensed
+- cdk (🥇25 · ⭐ 430 · ➕) - The Chemistry Development Kit. LGPL-2.1 cheminformatics Java
+- MPContribs (🥇23 · ⭐ 32 · ➕) - Platform for materials scientists to contribute and disseminate their materials data through Materials Project. MIT
+- Open Catalyst datasets (🥇18 · ⭐ 450 · ➕) - The datasets of the Open Catalyst project, OC20, OC22. CC-BY-4.0
+- GT4SD (🥇18 · ⭐ 230 · ➕) - GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process. MIT pre-trained drug-discovery rep-learn
+- CHGNet (🥈18 · ⭐ 79 · 🐣) - Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov. Custom MD pre-trained electrostatics magnetism structure-relaxation
+- escnn (🥈17 · ⭐ 200 · ➕) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. Custom
+- MatBench (🥈16 · ⭐ 77 · ➕) - Matbench: Benchmarks for materials science property prediction. MIT datasets benchmarking
+- NNPOps (🥈15 · ⭐ 61 · ➕) - High-performance operations for neural network potentials. MIT MD C++
+- AI for Science Resources (🥉14 · ⭐ 220 · 🐣) - List of resources for AI4Science research, including learning resources. GPL-3.0 license
+- Artificial Intelligence for Science (AIRS) (🥉14 · ⭐ 220 · 🐣) - Artificial Intelligence for Science (AIRS). GPL-3.0 license rep-learn generative MLIAP MD ML-DFT ML-WFT biomolecules
+- openmm-torch (🥈14 · ⭐ 130 · ➕) - OpenMM plugin to define forces with neural networks. Custom MLIAP C++
+- SPICE (🥈14 · ⭐ 89 · ➕) - A collection of QM data for training potential functions. MIT MLIAP MD
+- mp-pyrho (🥉14 · ⭐ 27 · ➕) - Custom ML-DFT
+- GlassPy (🥈13 · ⭐ 14 · ➕) - Python module for scientists working with glass materials. GPL-3.0
+- mat2vec (🥈12 · ⭐ 590 · ➕) - Supplementary Materials for Tshitoyan et al. Unsupervised word embeddings capture latent knowledge from materials.. MIT rep-learn
+- TensorMol (🥈12 · ⭐ 260 · 💀) - Tensorflow + Molecules = TensorMol. GPL-3.0 single-paper
+- OpenMM-ML (🥉10 · ⭐ 50 · ➕) - High level API for using machine learning models in OpenMM simulations. MIT MLIAP
+- ai4material_design (🥈10 · ⭐ 1 · ➕) - Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of.. Apache-2 pre-trained material-defect
+- EDM (🥉9 · ⭐ 290 · 💀) - E(3) Equivariant Diffusion Model for Molecule Generation in 3D. MIT
+- PROPhet (🥈9 · ⭐ 59 · 💀) - PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches. GPL-3.0 MLIAP MD single-paper C++
+- escnn_jax (🥉8 · ⭐ 21 · 🐣) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. Custom
+- SkipAtom (🥈8 · ⭐ 21 · 💀) - Distributed representations of atoms, inspired by the Skip-gram model. MIT
+- wfl (🥉8 · ⭐ 13 · ➕) - Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows. Unlicensed workflows HTC
+- EquiformerV2 (🥉6 · ⭐ 62 · 🐣) - [arXiv23] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. MIT
+- MEGAN (🥈6 · ⭐ 1 · ➕) - Minimal implementation of graph attention student model architecture. MIT XAI rep-learn
+- tensorfieldnetworks (🥉5 · ⭐ 140 · 💀) - MIT
+- EquivariantOperators.jl (🥉5 · ⭐ 17 · 💤) - MIT Julia
+- SciGlass (🥉5 · ⭐ 6 · ➕) - The database contains a vast set of data on the properties of glass materials. MIT
+- CraTENet (🥉5 · ⭐ 6 · ➕) - An attention-based deep neural network for thermoelectric transport properties. MIT transport-phenomena
+- closed-loop-acceleration-benchmarks (🥈5 · ➕) - Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational.. MIT materials-discovery active-learning single-paper
+- Closed-loop acceleration benchmarks (🥈5 · ➕) - Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational.. MIT materials-discovery active-learning single-paper
+- ML-for-CurieTemp-Predictions (🥉5 · 🐣) - Machine Learning Predictions of High-Curie-Temperature Materials. MIT single-paper magnetism
+- Linear vs blackbox (🥉4 · 💤) - Code and data related to the publication: Interpretable models for extrapolation in scientific machine learning. MIT XAI single-paper rep-eng
+- Atom2Vec (🥉3 · ⭐ 24 · 💀) - Atom2Vec: a simple way to describe atoms for machine learning. Unlicensed
+- sl_discovery (🥉3 · ⭐ 5 · 💤) - Data processing and models related to Quantifying the performance of machine learning models in materials discovery. Apache-2 materials-discovery single-paper
+- Element encoder (🥉3 · ⭐ 5 · 💀) - Autoencoder neural network to compress properties of atomic species into a vector representation. GPL-3.0 single-paper
+- DeepCDP (🥉3 · ⭐ 2 · ➕) - DeepCDP: Deep learning Charge Density Prediction. Unlicensed
+- MateriApps (➕) - Unlicensed
diff --git a/history/2023-08-25_projects.csv b/history/2023-08-25_projects.csv
index 7e1f024..f30542c 100644
--- a/history/2023-08-25_projects.csv
+++ b/history/2023-08-25_projects.csv
@@ -1,247 +1,292 @@
-,name,resource,category,homepage,description,projectrank,show,labels,license,github_id,github_url,created_at,updated_at,last_commit_pushed_at,commit_count,recent_commit_count,fork_count,watchers_count,pr_count,open_issue_count,closed_issue_count,star_count,latest_stable_release_published_at,latest_stable_release_number,release_count,contributor_count,pypi_id,conda_id,dependent_project_count,github_dependent_project_count,pypi_url,pypi_monthly_downloads,monthly_downloads,conda_url,conda_latest_release_published_at,conda_total_downloads,projectrank_placing,dockerhub_id,dockerhub_url,dockerhub_latest_release_published_at,dockerhub_stars,dockerhub_pulls,github_release_downloads,new_addition,trending,npm_id,npm_url,npm_monthly_downloads,docs_url,gitlab_id,gitlab_url,ignore
-0,Atomic Cluster Expansion,True,community,https://cortner.github.io/ACEweb/,Atomic Cluster Expansion (ACE) community homepage.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-1,Catalysis Hub,True,datasets,https://www.catalysis-hub.org/,A web-platform for sharing data and software for computational catalysis research!.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,crystals.ai,True,datasets,https://crystals.ai/,Curated datasets for reproducible AI in materials science.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,DeepChem Models,True,datasets,https://huggingface.co/DeepChem,,0,True,"['pre-trained', 'lm']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,JARVIS-Leaderboard,True,datasets,https://pages.nist.gov/jarvis_leaderboard/,This project provides benchmark-performances for materials science applications including Artificial Intelligence..,0,True,['benchmarking'],https://github.com/usnistgov/jarvis_leaderboard/blob/main/LICENSE.rst,usnistgov/jarvis_leaderboard,https://github.com/usnistgov/jarvis_leaderboard,2022-07-15 16:48:33,2023-08-16 14:30:48.000000,2023-08-16 14:26:39,673.0,202.0,24.0,5.0,228.0,4.0,2.0,39.0,2023-08-04 17:33:22,2023.08.01,21.0,24.0,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,matterverse.ai,True,datasets,https://matterverse.ai/,Database of yet-to-be-sythesized materials predicted using state-of-the-art machine learning algorithms.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-6,Quantum-Machine.org Datasets,True,datasets,http://quantum-machine.org/datasets/,"Collection of datasets, including QM7, QM7b, QM9, etc. MD and DFT data of various systems. Mostly small organic..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,sGDML Datasets,True,datasets,http://sgdml.org/#datasets,,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-8,Quantum Chemistry in the Age of Machine Learning,True,educational,https://www.elsevier.com/books-and-journals/book-companion/9780323900492,,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,Pre-trained OCP models,True,mliap,https://github.com/Open-Catalyst-Project/ocp/blob/main/MODELS.md,Pre-trained models released as part of the Open Catalyst Project.,0,True,['pre-trained'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,PyG Models,True,rep-learn,https://github.com/pyg-team/pytorch_geometric/tree/master/torch_geometric/nn/models,Representation learning models implemented in PyTorch Geometric.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,Deep Graph Library (DGL),,rep-learn,https://github.com/dmlc/dgl,"Python package built to ease deep learning on graph, on top of existing DL frameworks.",38,True,,Apache-2.0,dmlc/dgl,https://github.com/dmlc/dgl,2018-04-20 14:49:09,2023-08-25 11:11:33.000000,2023-08-25 06:39:54,3342.0,286.0,2790.0,170.0,3879.0,300.0,2027.0,12117.0,2023-08-15 07:31:40,1.1.2,75.0,273.0,dgl,dglteam/dgl,138.0,138.0,https://pypi.org/project/dgl,119087.0,124084.0,https://anaconda.org/dglteam/dgl,2023-08-17 01:25:44.402,279854.0,1.0,,,,,,,,,,,,,,,
-12,DeepChem,,general-tool,https://github.com/deepchem/deepchem,"Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology.",36,True,,MIT,deepchem/deepchem,https://github.com/deepchem/deepchem,2015-09-24 23:20:28,2023-08-25 09:45:10.000000,2023-08-23 19:15:57,9195.0,300.0,1451.0,148.0,1925.0,411.0,1204.0,4552.0,2022-12-01 13:22:37,2.7.1,18.0,224.0,deepchem,conda-forge/deepchem,244.0,244.0,https://pypi.org/project/deepchem,10705.0,13220.0,https://anaconda.org/conda-forge/deepchem,2023-06-16 19:18:02.015,102572.0,1.0,deepchemio/deepchem,https://hub.docker.com/r/deepchemio/deepchem,2022-03-11 05:24:00.723691,4.0,6948.0,,,,,,,,,,
-13,RDKit,,general-tool,https://github.com/rdkit/rdkit,,30,True,['lang-cpp'],BSD-3-Clause,rdkit/rdkit,https://github.com/rdkit/rdkit,2013-05-12 06:19:15,2023-08-25 11:09:17.000000,2023-08-25 04:09:19,7456.0,70.0,750.0,85.0,2784.0,838.0,2031.0,2161.0,2023-08-17 05:41:56,Release_2023_03_3,95.0,201.0,rdkit,rdkit/rdkit,2.0,2.0,https://pypi.org/project/rdkit,189831.0,212761.0,https://anaconda.org/rdkit/rdkit,2023-06-16 12:54:07.547,2521132.0,1.0,,,,,,1397.0,,,,,,,,,
-14,Matminer,,general-tool,https://github.com/hackingmaterials/matminer,Data mining for materials science.,29,True,,https://github.com/hackingmaterials/matminer/blob/main/LICENSE,hackingmaterials/matminer,https://github.com/hackingmaterials/matminer,2015-09-24 20:37:00,2023-08-21 08:07:39.000000,2023-06-27 15:45:29,4108.0,11.0,173.0,28.0,697.0,25.0,187.0,400.0,2023-06-27 15:36:52,0.9.0,66.0,49.0,matminer,conda-forge/matminer,222.0,222.0,https://pypi.org/project/matminer,16620.0,18041.0,https://anaconda.org/conda-forge/matminer,2023-06-27 20:24:06.404,48341.0,1.0,,,,,,,,,,,,,,,
-15,DeePMD-kit,,mliap,https://github.com/deepmodeling/deepmd-kit,A deep learning package for many-body potential energy representation and molecular dynamics.,28,True,['lang-cpp'],LGPL-3.0,deepmodeling/deepmd-kit,https://github.com/deepmodeling/deepmd-kit,2017-12-12 15:23:44,2023-08-25 03:39:10.000000,2023-08-08 03:52:54,2223.0,78.0,426.0,47.0,1223.0,50.0,390.0,1189.0,2023-08-08 05:00:42,2.2.3,37.0,60.0,deepmd-kit,deepmodeling/deepmd-kit,11.0,11.0,https://pypi.org/project/deepmd-kit,792.0,1384.0,https://anaconda.org/deepmodeling/deepmd-kit,2023-08-11 22:29:42.658,809.0,1.0,deepmodeling/deepmd-kit,https://hub.docker.com/r/deepmodeling/deepmd-kit,2023-08-13 18:58:55.024799,1.0,1981.0,24613.0,,,,,,,,,
-16,SchNetPack,,rep-learn,https://github.com/atomistic-machine-learning/schnetpack,SchNetPack - Deep Neural Networks for Atomistic Systems.,27,True,,MIT,atomistic-machine-learning/schnetpack,https://github.com/atomistic-machine-learning/schnetpack,2018-09-03 15:44:35,2023-08-18 13:26:08.000000,2023-08-17 11:11:14,1555.0,12.0,177.0,31.0,358.0,3.0,205.0,630.0,2023-04-25 11:23:00,2.0.3,7.0,31.0,schnetpack,,56.0,56.0,https://pypi.org/project/schnetpack,469.0,469.0,,,,1.0,,,,,,,,,,,,,,,
-17,QUIP,,general-tool,https://github.com/libAtoms/QUIP,libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io.,27,True,,GPL-2.0,libAtoms/QUIP,https://github.com/libAtoms/QUIP,2013-07-02 15:21:59,2023-07-18 16:21:18.000000,2023-07-18 16:06:47,10831.0,44.0,117.0,27.0,163.0,88.0,335.0,297.0,2023-06-15 19:11:24,0.9.14,15.0,79.0,quippy-ase,,22.0,22.0,https://pypi.org/project/quippy-ase,1439.0,1527.0,,,,2.0,libatomsquip/quip,https://hub.docker.com/r/libatomsquip/quip,2023-04-24 21:25:17.345957,4.0,9847.0,346.0,,,,,,,,,
-18,paper-qa,,lm,https://github.com/whitead/paper-qa,LLM Chain for answering questions from documents with citations.,26,True,,Apache-2.0,whitead/paper-qa,https://github.com/whitead/paper-qa,2023-02-05 01:07:25,2023-08-24 05:38:45.000000,2023-08-24 04:38:32,165.0,44.0,270.0,38.0,75.0,39.0,59.0,2962.0,2023-08-24 05:38:45,3.8.0,63.0,12.0,paper-qa,,25.0,25.0,https://pypi.org/project/paper-qa,3711.0,3711.0,,,,1.0,,,,,,,,,,,,,,,
-19,e3nn,,rep-learn,https://github.com/e3nn/e3nn,A modular framework for neural networks with Euclidean symmetry.,24,True,,MIT,e3nn/e3nn,https://github.com/e3nn/e3nn,2020-01-31 13:06:42,2023-08-15 13:48:27.000000,2023-08-15 13:48:22,2156.0,4.0,106.0,19.0,202.0,17.0,129.0,726.0,2022-12-12 21:42:03,0.5.1,28.0,28.0,e3nn,conda-forge/e3nn,102.0,102.0,https://pypi.org/project/e3nn,13184.0,13756.0,https://anaconda.org/conda-forge/e3nn,2023-06-18 08:41:30.723,8587.0,1.0,,,,,,,,,,,,,,,
-20,AlphaFold,,biomolecules,https://github.com/deepmind/alphafold,Open source code for AlphaFold.,23,True,,Apache-2.0,deepmind/alphafold,https://github.com/deepmind/alphafold,2021-06-17 14:06:06,2023-08-10 17:19:05.000000,2023-08-10 13:28:00,125.0,5.0,1837.0,204.0,81.0,165.0,565.0,10656.0,2023-04-05 09:45:53,2.3.2,13.0,19.0,,,6.0,6.0,,,,,,,1.0,,,,,,,,,,,,,,,
-21,JAX-MD,,md,https://github.com/jax-md/jax-md,"Differentiable, Hardware Accelerated, Molecular Dynamics.",23,True,,Apache-2.0,jax-md/jax-md,https://github.com/jax-md/jax-md,2019-05-13 21:03:37,2023-08-22 22:45:59.000000,2023-08-22 22:45:58,836.0,42.0,159.0,50.0,153.0,60.0,73.0,980.0,2022-11-27 12:42:00,jax-md-v0.2.24,7.0,28.0,jax-md,,33.0,33.0,https://pypi.org/project/jax-md,1891.0,1891.0,,,,1.0,,,,,,,,,,,,,,,
-22,dgl-lifesci,,rep-learn,https://github.com/awslabs/dgl-lifesci,Python package for graph neural networks in chemistry and biology.,23,True,,Apache-2.0,awslabs/dgl-lifesci,https://github.com/awslabs/dgl-lifesci,2020-04-23 07:14:21,2023-06-12 18:56:16.000000,2023-04-16 03:55:52,236.0,,133.0,17.0,139.0,24.0,56.0,606.0,2023-02-13 08:45:17,0.3.2,8.0,22.0,dgllife,,108.0,108.0,https://pypi.org/project/dgllife,12889.0,12889.0,,,,1.0,,,,,,,,,,,,,,,
-23,JARVIS-Tools,,general-tool,https://github.com/usnistgov/jarvis,JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications:..,23,True,,https://github.com/usnistgov/jarvis/blob/master/LICENSE.rst,usnistgov/jarvis,https://github.com/usnistgov/jarvis,2017-06-22 19:34:02,2023-08-20 02:08:18.000000,2023-08-20 02:07:14,2093.0,6.0,105.0,27.0,221.0,38.0,43.0,239.0,2023-08-11 17:26:26,2023.08.01,69.0,15.0,jarvis-tools,conda-forge/jarvis-tools,60.0,60.0,https://pypi.org/project/jarvis-tools,15833.0,17448.0,https://anaconda.org/conda-forge/jarvis-tools,2023-06-16 19:23:23.093,54937.0,2.0,,,,,,,,,,,,,,,
-24,Best-of Machine Learning with Python,,community,https://github.com/ml-tooling/best-of-ml-python,A ranked list of awesome machine learning Python libraries. Updated weekly.,22,True,"['general-ml', 'lang-py']",CC-BY-4.0,ml-tooling/best-of-ml-python,https://github.com/ml-tooling/best-of-ml-python,2020-11-29 19:41:36,2023-08-24 15:01:00.000000,2023-08-24 15:00:59,412.0,29.0,2040.0,376.0,216.0,15.0,32.0,14233.0,2023-08-24 15:01:06,2023.08.24,100.0,41.0,,,,,,,,,,,1.0,,,,,,,True,,,,,,,,
-25,NVIDIA Deep Learning Examples for Tensor Cores,,rep-learn,https://github.com/NVIDIA/DeepLearningExamples#graph-neural-networks,State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and..,22,True,"['educational', 'drug-discovery']",https://github.com/NVIDIA/DeepLearningExamples/blob/master/DGLPyTorch/DrugDiscovery/SE3Transformer/LICENSE,NVIDIA/DeepLearningExamples,https://github.com/NVIDIA/DeepLearningExamples,2018-05-02 17:04:05,2023-08-23 10:13:19.000000,2023-08-23 10:09:12,1424.0,12.0,2755.0,289.0,523.0,217.0,556.0,11389.0,,,,115.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,
-26,TorchANI,,mliap,https://github.com/aiqm/torchani,Accurate Neural Network Potential on PyTorch.,22,True,,MIT,aiqm/torchani,https://github.com/aiqm/torchani,2018-04-02 15:43:04,2023-06-25 04:24:10.593000,2023-03-21 14:30:59,433.0,,109.0,27.0,478.0,17.0,137.0,392.0,2023-04-13 18:10:53,2.2.3,23.0,16.0,torchani,conda-forge/torchani,25.0,25.0,https://pypi.org/project/torchani,1752.0,7583.0,https://anaconda.org/conda-forge/torchani,2023-06-25 04:24:10.593,209946.0,1.0,,,,,,,,,,,,,,,
-27,DScribe,,rep-eng,https://github.com/SINGROUP/dscribe,DScribe is a python package for creating machine learning descriptors for atomistic systems.,22,True,,Apache-2.0,SINGROUP/dscribe,https://github.com/SINGROUP/dscribe,2017-05-08 08:29:51,2023-08-14 19:17:34.000000,2023-07-19 15:24:32,1284.0,9.0,78.0,21.0,24.0,8.0,77.0,341.0,,,13.0,18.0,dscribe,conda-forge/dscribe,145.0,145.0,https://pypi.org/project/dscribe,5358.0,7424.0,https://anaconda.org/conda-forge/dscribe,2023-07-19 19:51:35.626,76449.0,1.0,,,,,,,,,,,,,,,
-28,MAML,,general-tool,https://github.com/materialsvirtuallab/maml,"Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.",22,True,,BSD-3-Clause,materialsvirtuallab/maml,https://github.com/materialsvirtuallab/maml,2020-01-25 15:04:21,2023-08-22 13:52:36.000000,2023-08-16 03:01:53,1553.0,130.0,58.0,21.0,497.0,4.0,58.0,261.0,2022-09-20 18:44:37,2022.9.20,12.0,27.0,maml,,4.0,4.0,https://pypi.org/project/maml,128.0,128.0,,,,2.0,,,,,,,,,,,,,,,
-29,DP-GEN,,mliap,https://github.com/deepmodeling/dpgen,The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field.,22,True,['workflows'],LGPL-3.0,deepmodeling/dpgen,https://github.com/deepmodeling/dpgen,2019-06-13 11:43:56,2023-08-25 06:13:47.000000,2023-03-21 14:37:04,1958.0,,154.0,13.0,718.0,42.0,194.0,229.0,2023-03-22 17:49:22,0.11.1,16.0,59.0,dpgen,deepmodeling/dpgen,4.0,4.0,https://pypi.org/project/dpgen,184.0,221.0,https://anaconda.org/deepmodeling/dpgen,2023-06-16 19:27:03.566,162.0,1.0,,,,,,1430.0,,,,,,,,,
-30,kgcnn,,rep-learn,https://github.com/aimat-lab/gcnn_keras,Graph convolution with tf.keras.,22,True,,MIT,aimat-lab/gcnn_keras,https://github.com/aimat-lab/gcnn_keras,2020-07-17 11:12:46,2023-08-17 09:42:40.000000,2023-08-17 09:38:11,2758.0,76.0,24.0,7.0,30.0,5.0,72.0,80.0,2023-08-01 15:42:10,3.0.2,23.0,7.0,kgcnn,,15.0,15.0,https://pypi.org/project/kgcnn,408.0,408.0,,,,1.0,,,,,,,,,,,,,,,
-31,DM21,,ml-dft,https://github.com/deepmind/deepmind-research/tree/master/density_functional_approximation_dm21,This package provides a PySCF interface to the DM21 (DeepMind 21) family of exchange-correlation functionals described..,21,True,,Apache-2.0,deepmind/deepmind-research,https://github.com/deepmind/deepmind-research,2019-01-15 09:54:13,2023-08-17 01:03:23.000000,2023-06-02 17:04:50,369.0,13.0,2397.0,338.0,147.0,154.0,134.0,12053.0,,,,92.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,
-32,MEGNet,,mliap,https://github.com/materialsvirtuallab/megnet,Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals.,21,True,,BSD-3-Clause,materialsvirtuallab/megnet,https://github.com/materialsvirtuallab/megnet,2018-12-12 21:31:28,2023-04-27 02:39:17.000000,2023-04-27 02:39:17,1146.0,,140.0,24.0,314.0,17.0,57.0,455.0,2022-11-16 21:24:36,1.3.2,34.0,13.0,megnet,,69.0,69.0,https://pypi.org/project/megnet,692.0,692.0,,,,1.0,,,,,,,,,,,,,,,
-33,dpdata,,data-structures,https://github.com/deepmodeling/dpdata,"Manipulating multiple atomic simulation data formats, including DeePMD-kit, VASP, LAMMPS, ABACUS, etc.",21,True,,LGPL-3.0,deepmodeling/dpdata,https://github.com/deepmodeling/dpdata,2019-04-12 13:24:23,2023-08-23 02:14:20.000000,2023-05-04 01:19:46,604.0,,101.0,8.0,369.0,22.0,46.0,138.0,2023-05-04 01:23:26,0.2.15,22.0,45.0,dpdata,deepmodeling/dpdata,100.0,100.0,https://pypi.org/project/dpdata,2301.0,2321.0,https://anaconda.org/deepmodeling/dpdata,2023-06-16 19:27:06.316,489.0,1.0,,,,,,,,,,,,,,,
-34,NequIP,,mliap,https://github.com/mir-group/nequip,NequIP is a code for building E(3)-equivariant interatomic potentials.,20,True,,MIT,mir-group/nequip,https://github.com/mir-group/nequip,2021-03-15 23:44:39,2023-08-17 18:10:06.000000,2023-03-26 21:37:08,1670.0,,90.0,18.0,149.0,12.0,48.0,420.0,2022-12-20 18:52:46,0.5.6,14.0,8.0,nequip,conda-forge/nequip,14.0,14.0,https://pypi.org/project/nequip,505.0,724.0,https://anaconda.org/conda-forge/nequip,2023-06-18 08:41:30.787,3288.0,1.0,,,,,,,,,,,,,,,
-35,DeepQMC,,ml-wft,https://github.com/deepqmc/deepqmc,Deep learning quantum Monte Carlo for electrons in real space.,20,True,,MIT,deepqmc/deepqmc,https://github.com/deepqmc/deepqmc,2019-12-06 14:50:59,2023-08-25 12:44:41.000000,2023-08-08 09:18:02,1278.0,66.0,56.0,22.0,123.0,6.0,30.0,297.0,2023-01-04 09:38:38,1.0.1,7.0,13.0,deepqmc,,1.0,1.0,https://pypi.org/project/deepqmc,50.0,50.0,,,,1.0,,,,,,,,,,,,,,,
-36,MatGL (Materials Graph Library),,rep-learn,https://github.com/materialsvirtuallab/matgl,Graph deep learning library for materials.,20,True,,BSD-3-Clause,materialsvirtuallab/matgl,https://github.com/materialsvirtuallab/matgl,2022-08-29 18:36:05,2023-08-24 17:04:44.000000,2023-08-24 17:04:43,831.0,439.0,24.0,7.0,109.0,1.0,33.0,110.0,2023-08-22 14:09:18,0.8.3,21.0,11.0,m3gnet,,2.0,2.0,https://pypi.org/project/m3gnet,804.0,804.0,,,,2.0,,,,,,,,,,,,,,,
-37,ocp,,rep-learn,https://github.com/Open-Catalyst-Project/ocp,ocp is the Open Catalyst Projects library of state-of-the-art machine learning algorithms for catalysis.,19,True,,MIT,Open-Catalyst-Project/ocp,https://github.com/Open-Catalyst-Project/ocp,2019-09-26 04:47:27,2023-08-24 22:21:36.000000,2023-08-24 22:13:39,697.0,23.0,166.0,24.0,442.0,12.0,113.0,448.0,2022-10-01 03:00:41,0.1.0,4.0,31.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-38,exmol,,xai,https://github.com/ur-whitelab/exmol,Explainer for black box models that predict molecule properties.,19,True,,MIT,ur-whitelab/exmol,https://github.com/ur-whitelab/exmol,2021-08-03 17:56:06,2023-06-19 20:50:50.000000,2023-06-19 19:49:27,188.0,1.0,37.0,9.0,73.0,10.0,57.0,251.0,2023-06-19 20:50:51,3.0.3,27.0,7.0,exmol,,12.0,12.0,https://pypi.org/project/exmol,421.0,421.0,,,,1.0,,,,,,,,,,,,,,,
-39,FLARE,,active-learning,https://github.com/mir-group/flare,An open-source Python package for creating fast and accurate interatomic potentials.,19,True,"['lang-cpp', 'mliap']",MIT,mir-group/flare,https://github.com/mir-group/flare,2018-08-30 23:40:56,2023-07-20 11:31:36.000000,2023-05-26 02:06:09,4382.0,,55.0,18.0,184.0,19.0,160.0,240.0,2022-04-21 18:33:10,0.2.4,5.0,36.0,,,10.0,10.0,,,0.0,,,,1.0,,,,,,1.0,,1.0,,,,,,,
-40,FitSNAP,,md,https://github.com/FitSNAP/FitSNAP,Software for generating SNAP machine-learning interatomic potentials.,19,True,,GPL-2.0,FitSNAP/FitSNAP,https://github.com/FitSNAP/FitSNAP,2019-09-12 14:46:18,2023-08-25 00:10:17.000000,2023-08-22 15:25:33,1284.0,55.0,42.0,7.0,164.0,8.0,54.0,121.0,2023-06-28 16:00:48,3.1.0,7.0,24.0,,conda-forge/fitsnap3,,,,,132.0,https://anaconda.org/conda-forge/fitsnap3,2023-06-16 00:19:04.615,4351.0,2.0,,,,,,5.0,,,,,,,,,
-41,Graph-based Deep Learning Literature,,community,https://github.com/naganandy/graph-based-deep-learning-literature,links to conference publications in graph-based deep learning.,18,True,"['general-ml', 'rep-learn']",MIT,naganandy/graph-based-deep-learning-literature,https://github.com/naganandy/graph-based-deep-learning-literature,2017-12-01 14:48:35,2023-08-24 11:07:22.000000,2023-08-13 09:28:41,7606.0,35.0,700.0,242.0,21.0,,13.0,4294.0,,,,11.0,,,,,,,,,,,2.0,,,,,,,True,,,,,,,,
-42,M3GNet,,mliap,https://github.com/materialsvirtuallab/m3gnet,Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art..,18,True,,BSD-3-Clause,materialsvirtuallab/m3gnet,https://github.com/materialsvirtuallab/m3gnet,2022-01-18 18:10:58,2023-06-06 23:56:08.000000,2023-06-06 23:56:03,261.0,4.0,50.0,10.0,33.0,15.0,20.0,173.0,2022-11-17 23:25:35,0.2.4,16.0,14.0,m3gnet,,16.0,16.0,https://pypi.org/project/m3gnet,804.0,804.0,,,,2.0,,,,,,,,,,,,,,,
-43,ALIGNN,,rep-learn,https://github.com/usnistgov/alignn,Atomistic Line Graph Neural Network.,18,True,,https://github.com/usnistgov/alignn/blob/main/LICENSE.rst,usnistgov/alignn,https://github.com/usnistgov/alignn,2021-04-19 20:08:09,2023-08-11 18:09:14.000000,2023-08-11 04:50:09,595.0,78.0,64.0,11.0,88.0,23.0,23.0,144.0,2023-08-11 04:51:42,2023.08.01,40.0,7.0,alignn,,4.0,4.0,https://pypi.org/project/alignn,510.0,510.0,,,,2.0,,,,,,,,,,,,,,,
-44,e3nn-jax,,rep-learn,https://github.com/e3nn/e3nn-jax,jax library for E3 Equivariant Neural Networks.,18,True,,Apache-2.0,e3nn/e3nn-jax,https://github.com/e3nn/e3nn-jax,2021-06-08 13:21:51,2023-08-11 13:46:24.000000,2023-08-11 13:46:20,894.0,63.0,13.0,9.0,29.0,1.0,7.0,121.0,2023-06-24 17:06:31,0.19.3,35.0,4.0,e3nn-jax,,,,https://pypi.org/project/e3nn-jax,2243.0,2243.0,,,,2.0,,,,,,,,,,,,,,,
-45,Scikit-Matter,,general-tool,https://github.com/scikit-learn-contrib/scikit-matter,A collection of scikit-learn compatible utilities that implement methods born out of the materials science and..,18,True,['scikit-learn'],BSD-3-Clause,scikit-learn-contrib/scikit-matter,https://github.com/scikit-learn-contrib/scikit-matter,2020-10-12 19:23:26,2023-08-25 09:05:56.000000,2023-08-25 09:05:38,362.0,35.0,15.0,17.0,147.0,12.0,56.0,60.0,2023-08-24 17:18:49,0.2.0,7.0,12.0,skmatter,conda-forge/skmatter,5.0,5.0,https://pypi.org/project/skmatter,330.0,412.0,https://anaconda.org/conda-forge/skmatter,2023-08-24 19:08:29.551,412.0,2.0,,,,,,,,,,,,,,,
-46,MALA,,ml-dft,https://github.com/mala-project/mala,Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data.,18,True,,BSD-3-Clause,mala-project/mala,https://github.com/mala-project/mala,2021-03-31 11:40:38,2023-08-04 14:05:18.000000,2023-08-04 14:04:12,2070.0,85.0,19.0,9.0,248.0,23.0,208.0,50.0,2022-10-18 07:04:37,1.1.0,7.0,41.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,
-47,Uni-Mol,,rep-learn,https://github.com/dptech-corp/Uni-Mol,Official Repository for the Uni-Mol Series Methods.,17,True,['pre-trained'],MIT,dptech-corp/Uni-Mol,https://github.com/dptech-corp/Uni-Mol,2022-05-22 13:26:41,2023-08-25 09:28:29.000000,2023-08-25 08:28:15,81.0,10.0,71.0,19.0,56.0,27.0,67.0,398.0,2023-07-07 09:02:23,0.2,2.0,8.0,,,,,,,497.0,,,,2.0,,,,,,4974.0,,1.0,,,,,,,
-48,MoLeR,,generative,https://github.com/microsoft/molecule-generation,Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation.,17,True,,MIT,microsoft/molecule-generation,https://github.com/microsoft/molecule-generation,2022-02-17 19:16:29,2023-08-09 14:17:04.000000,2023-08-09 14:17:01,63.0,4.0,31.0,11.0,33.0,6.0,24.0,199.0,2023-06-18 21:03:46,0.4.0,4.0,5.0,molecule-generation,,,,https://pypi.org/project/molecule-generation,184.0,184.0,,,,1.0,,,,,,,,,,,,,,,
-49,KFAC-JAX,,math,https://github.com/deepmind/kfac-jax,Second Order Optimization and Curvature Estimation with K-FAC in JAX.,17,True,,Apache-2.0,deepmind/kfac-jax,https://github.com/deepmind/kfac-jax,2022-03-18 10:19:24,2023-08-23 12:47:49.000000,2023-08-22 17:11:59,140.0,20.0,10.0,6.0,151.0,4.0,4.0,151.0,2023-05-16 18:03:40,0.0.5,4.0,11.0,kfac-jax,,6.0,6.0,https://pypi.org/project/kfac-jax,604.0,604.0,,,,1.0,,,,,,,,,,,,,,,
-50,Chemiscope,,visualization,https://github.com/lab-cosmo/chemiscope,An interactive structure/property explorer for materials and molecules.,17,True,['lang-js'],BSD-3-Clause,lab-cosmo/chemiscope,https://github.com/lab-cosmo/chemiscope,2019-10-03 09:59:42,2023-08-25 08:20:51.000000,2023-07-25 15:28:49,648.0,3.0,26.0,15.0,183.0,40.0,66.0,86.0,2023-03-15 15:39:40,0.5.2,12.0,17.0,,,5.0,5.0,,,40.0,,,,1.0,,,,,,135.0,,,chemiscope,https://www.npmjs.com/package/chemiscope,37.0,,,,
-51,MAST-ML,,general-tool,https://github.com/uw-cmg/MAST-ML,MAterials Simulation Toolkit for Machine Learning (MAST-ML).,17,True,,MIT,uw-cmg/MAST-ML,https://github.com/uw-cmg/MAST-ML,2017-02-16 17:03:57,2023-07-28 19:11:53.000000,2023-07-28 18:33:43,3162.0,3.0,50.0,13.0,36.0,22.0,191.0,86.0,2023-05-01 21:32:25,3.1.7,6.0,19.0,,,6.0,6.0,,,2.0,,,,2.0,,,,,,81.0,,,,,,,,,
-52,QML,,general-tool,https://github.com/qmlcode/qml,QML: Quantum Machine Learning.,16,False,,MIT,qmlcode/qml,https://github.com/qmlcode/qml,2017-04-22 04:48:38,2023-07-26 12:01:42.000000,2018-09-10 11:14:35,75.0,,78.0,23.0,101.0,27.0,19.0,185.0,,,3.0,2.0,qml,,18.0,18.0,https://pypi.org/project/qml,246.0,246.0,,,,2.0,,,,,,,,,,,,,,,
-53,XenonPy,,general-tool,https://github.com/yoshida-lab/XenonPy,XenonPy is a Python Software for Materials Informatics.,16,True,,BSD-3-Clause,yoshida-lab/XenonPy,https://github.com/yoshida-lab/XenonPy,2018-01-17 10:13:29,2023-05-21 15:54:32.000000,2023-05-21 15:53:00,682.0,,57.0,12.0,182.0,16.0,66.0,107.0,2023-05-21 15:54:32,0.6.8,45.0,10.0,xenonpy,,,,https://pypi.org/project/xenonpy,165.0,182.0,,,,2.0,,,,,,1168.0,,,,,,,,,
-54,gpax,,math,https://github.com/ziatdinovmax/gpax,Gaussian Processes for Experimental Sciences.,16,True,"['probabilistic', 'active-learning']",MIT,ziatdinovmax/gpax,https://github.com/ziatdinovmax/gpax,2021-10-28 13:43:18,2023-08-24 05:24:19.000000,2023-08-07 01:30:41,452.0,101.0,15.0,3.0,29.0,2.0,5.0,101.0,2023-07-09 23:50:56,0.0.7,7.0,,gpax,,,,https://pypi.org/project/gpax,129.0,129.0,,,,2.0,,,,,,,,,,,,,,,
-55,CatLearn,,rep-eng,https://github.com/SUNCAT-Center/CatLearn,,16,True,['surface-science'],GPL-3.0,SUNCAT-Center/CatLearn,https://github.com/SUNCAT-Center/CatLearn,2018-04-20 04:16:14,2023-07-25 21:09:47.000000,2023-02-07 09:31:25,1960.0,,50.0,19.0,79.0,9.0,16.0,92.0,2020-03-27 09:26:03,0.6.2,8.0,22.0,catlearn,,4.0,4.0,https://pypi.org/project/catlearn,87.0,87.0,,,,1.0,,,,,,,,,,,,,,,
-56,benchmarking-gnns,,rep-learn,https://github.com/graphdeeplearning/benchmarking-gnns,Repository for benchmarking graph neural networks.,15,False,"['single-paper', 'benchmarking']",MIT,graphdeeplearning/benchmarking-gnns,https://github.com/graphdeeplearning/benchmarking-gnns,2020-03-03 03:42:50,2023-06-22 04:03:53.000000,2022-05-10 13:22:20,45.0,,415.0,59.0,17.0,4.0,60.0,2234.0,,,,5.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-57,FermiNet,,ml-wft,https://github.com/deepmind/ferminet,An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations.,15,True,,Apache-2.0,deepmind/ferminet,https://github.com/deepmind/ferminet,2020-10-06 12:21:06,2023-08-14 14:54:45.000000,2023-08-14 13:41:50,192.0,12.0,98.0,34.0,27.0,,39.0,575.0,,,,18.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-58,Uni-Fold,,biomolecules,https://github.com/dptech-corp/Uni-Fold,An open-source platform for developing protein models beyond AlphaFold.,15,True,,Apache-2.0,dptech-corp/Uni-Fold,https://github.com/dptech-corp/Uni-Fold,2022-07-30 03:37:29,2023-08-23 10:48:55.000000,2023-06-19 13:56:13,95.0,2.0,47.0,7.0,72.0,9.0,47.0,282.0,2022-10-19 12:44:31,2.2.0,3.0,7.0,,,,,,,213.0,,,,3.0,,,,,,2130.0,,,,,,,,,
-59,sGDML,,mliap,https://github.com/stefanch/sGDML,sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model.,15,True,,MIT,stefanch/sGDML,https://github.com/stefanch/sGDML,2018-07-11 15:20:30,2023-08-07 08:17:29.000000,2023-06-08 12:49:46,199.0,1.0,34.0,9.0,12.0,5.0,11.0,123.0,2023-06-08 12:51:43,1.0.1,14.0,7.0,sgdml,,8.0,8.0,https://pypi.org/project/sgdml,108.0,108.0,,,,2.0,,,,,,,,,,,,,,,
-60,PyXtalFF,,mliap,https://github.com/MaterSim/PyXtal_FF,Machine Learning Interatomic Potential Predictions.,15,True,,MIT,MaterSim/PyXtal_FF,https://github.com/MaterSim/PyXtal_FF,2019-01-08 08:43:35,2023-08-17 01:22:23.000000,2023-08-17 01:22:18,559.0,6.0,19.0,9.0,2.0,9.0,51.0,71.0,2023-06-09 17:17:24,0.2.3,19.0,8.0,pyxtal_ff,,,,https://pypi.org/project/pyxtal_ff,48.0,48.0,,,,2.0,,,,,,,,1.0,,,,,,,
-61,DADApy,,unsupervised,https://github.com/sissa-data-science/DADApy,Distance-based Analysis of DAta-manifolds in python.,15,True,,Apache-2.0,sissa-data-science/DADApy,https://github.com/sissa-data-science/DADApy,2021-02-16 17:45:23,2023-08-15 08:50:41.000000,2023-08-15 08:50:41,643.0,12.0,8.0,8.0,73.0,9.0,15.0,69.0,2023-05-25 16:37:17,0.2.0,3.0,15.0,dadapy,,2.0,2.0,https://pypi.org/project/dadapy,37.0,37.0,,,,1.0,,,,,,,,,,,,,,,
-62,MACE,,mliap,https://github.com/ACEsuit/mace,MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.,14,True,,MIT,ACEsuit/mace,https://github.com/ACEsuit/mace,2022-06-21 18:44:34,2023-08-22 09:08:41.000000,2023-08-17 09:53:26,210.0,3.0,61.0,18.0,72.0,16.0,38.0,184.0,2023-02-09 12:24:53,0.2.0,1.0,11.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-63,SISSO,,rep-eng,https://github.com/rouyang2017/SISSO,A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models.,14,True,['lang-fortran'],Apache-2.0,rouyang2017/SISSO,https://github.com/rouyang2017/SISSO,2017-10-16 11:31:57,2023-07-26 04:28:51.000000,2023-07-26 04:28:51,164.0,14.0,60.0,6.0,2.0,,50.0,175.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-64,Automatminer,,general-tool,https://github.com/hackingmaterials/automatminer,An automatic engine for predicting materials properties.,14,False,,https://github.com/hackingmaterials/automatminer/blob/main/LICENSE,hackingmaterials/automatminer,https://github.com/hackingmaterials/automatminer,2018-05-10 18:27:08,2022-12-07 10:00:21.000000,2022-01-06 19:39:49,1666.0,,43.0,12.0,232.0,36.0,138.0,125.0,2020-07-28 02:19:07,1.0.3.20200727,17.0,13.0,automatminer,,6.0,6.0,https://pypi.org/project/automatminer,80.0,80.0,,,,3.0,,,,,,,,,,,,,,,
-65,SpheriCart,,math,https://github.com/lab-cosmo/sphericart,Multi-language library for the calculation of spherical harmonics in Cartesian coordinates.,14,True,,Apache-2.0,lab-cosmo/sphericart,https://github.com/lab-cosmo/sphericart,2023-02-04 15:15:25,2023-08-18 12:23:31.000000,2023-08-18 12:20:46,317.0,11.0,3.0,4.0,53.0,8.0,6.0,40.0,2023-04-26 12:06:09,0.3.0,1.0,6.0,sphericart,,1.0,1.0,https://pypi.org/project/sphericart,25.0,25.0,,,,2.0,,,,,,,,,,,,,,,
-66,Ultra-Fast Force Fields (UF3),,mliap,https://github.com/uf3/uf3,UF3: a python library for generating ultra-fast interatomic potentials.,14,True,,Apache-2.0,uf3/uf3,https://github.com/uf3/uf3,2021-10-01 13:21:44,2023-08-24 18:39:48.000000,2023-08-24 18:39:48,462.0,31.0,14.0,4.0,30.0,10.0,17.0,30.0,2022-09-29 10:22:37,0.3.2,3.0,6.0,uf3,,,,https://pypi.org/project/uf3,25.0,25.0,,,,2.0,,,,,,,,,,,,,,,
-67,KLIFF,,mliap,https://github.com/openkim/kliff,KIM-based Learning-Integrated Fitting Framework (KLIFF).,14,True,"['probabilistic', 'workflows']",LGPL-2.1,openkim/kliff,https://github.com/openkim/kliff,2017-08-01 20:33:58,2023-08-14 00:02:14.000000,2023-08-09 17:09:55,982.0,31.0,17.0,2.0,96.0,17.0,16.0,27.0,2022-10-07 05:16:11,0.4.1,16.0,10.0,kliff,conda-forge/kliff,,,https://pypi.org/project/kliff,90.0,1484.0,https://anaconda.org/conda-forge/kliff,2023-06-16 16:19:12.982,62771.0,2.0,,,,,,,,,,,,,,,
-68,Polynomials4ML.jl,,math,https://github.com/ACEsuit/Polynomials4ML.jl,"Polynomials for ML: fast evaluation, batching, differentiation.",14,True,['lang-julia'],MIT,ACEsuit/Polynomials4ML.jl,https://github.com/ACEsuit/Polynomials4ML.jl,2022-09-20 23:05:53,2023-08-09 23:54:30.000000,2023-08-09 05:03:18,278.0,134.0,2.0,3.0,24.0,16.0,22.0,10.0,2023-07-29 00:42:23,0.2.2,6.0,8.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-69,n2p2,,mliap,https://github.com/CompPhysVienna/n2p2,n2p2 - A Neural Network Potential Package.,13,True,['lang-cpp'],GPL-3.0,CompPhysVienna/n2p2,https://github.com/CompPhysVienna/n2p2,2018-07-25 12:29:17,2023-05-11 16:26:02.000000,2022-09-05 10:56:20,387.0,,66.0,12.0,53.0,54.0,85.0,184.0,2022-05-23 12:53:39,2.2.0,11.0,9.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-70,DeepH-pack,,ml-dft,https://github.com/mzjb/DeepH-pack,Deep neural networks for density functional theory Hamiltonian.,13,True,['lang-julia'],LGPL-3.0,mzjb/DeepH-pack,https://github.com/mzjb/DeepH-pack,2022-05-13 02:51:32,2023-08-03 05:36:54.000000,2023-07-11 08:11:15,54.0,9.0,25.0,4.0,13.0,5.0,29.0,130.0,2023-07-11 08:13:06,0.2.2,2.0,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-71,AMPtorch,,general-tool,https://github.com/ulissigroup/amptorch,AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch.,13,True,,GPL-3.0,ulissigroup/amptorch,https://github.com/ulissigroup/amptorch,2019-01-24 15:15:48,2023-07-16 02:11:38.000000,2023-07-16 02:08:13,759.0,7.0,31.0,9.0,99.0,4.0,25.0,53.0,2023-07-16 02:11:38,1.0,3.0,14.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-72,So3krates (MLFF),,mliap,https://github.com/thorben-frank/mlff,Build neural networks for machine learning force fields with JAX.,13,True,,MIT,thorben-frank/mlff,https://github.com/thorben-frank/mlff,2022-09-30 07:40:17,2023-08-18 06:21:23.000000,2023-08-18 06:21:16,97.0,60.0,6.0,3.0,9.0,1.0,4.0,35.0,2023-07-12 08:34:56,0.2.0,1.0,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-73,aviary,,materials-discovery,https://github.com/CompRhys/aviary,The Wren sits on its Roost in the Aviary.,13,True,,MIT,CompRhys/aviary,https://github.com/CompRhys/aviary,2021-09-28 12:29:05,2023-08-10 01:55:31.000000,2023-08-10 01:50:53,603.0,5.0,7.0,2.0,48.0,4.0,22.0,27.0,2023-08-10 01:55:58,0.1.1,4.0,4.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,
-74,Equistore,,data-structures,https://github.com/lab-cosmo/equistore,Storage format for equivariant atomistic machine learning.,13,True,,BSD-3-Clause,lab-cosmo/equistore,https://github.com/lab-cosmo/equistore,2022-03-01 15:58:28,2023-08-25 08:59:28.000000,2023-08-24 12:06:48,362.0,89.0,12.0,15.0,242.0,40.0,60.0,24.0,,,,16.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-75,CCS_fit,,mliap,https://github.com/Teoroo-CMC/CCS,Curvature Constrained Splines.,13,True,,GPL-3.0,Teoroo-CMC/CCS,https://github.com/Teoroo-CMC/CCS,2021-12-13 14:29:53,2023-08-23 07:24:46.000000,2023-08-23 07:24:44,752.0,21.0,8.0,2.0,10.0,8.0,6.0,5.0,2023-08-23 07:24:47,0.22.1,100.0,8.0,ccs_fit,,,,https://pypi.org/project/ccs_fit,295.0,337.0,,,,2.0,,,,,,379.0,,,,,,,,,
-76,Deep Learning for Molecules and Materials Book,,educational,https://dmol.pub/,Deep learning for molecules and materials book.,12,True,,https://github.com/whitead/dmol-book/blob/main/LICENSE,whitead/dmol-book,https://github.com/whitead/dmol-book,2020-08-19 19:24:32,2023-07-02 18:02:57.000000,2023-07-02 18:02:56,558.0,3.0,96.0,17.0,92.0,25.0,128.0,520.0,,,,19.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,
-77,Crystal Graph Convolutional Neural Networks (CGCNN),,rep-learn,https://github.com/txie-93/cgcnn,Crystal graph convolutional neural networks for predicting material properties.,12,False,,MIT,txie-93/cgcnn,https://github.com/txie-93/cgcnn,2018-03-14 20:41:21,2021-09-06 05:23:51.000000,2021-09-06 05:23:38,25.0,,247.0,22.0,7.0,15.0,18.0,496.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-78,ASAP,,unsupervised,https://github.com/BingqingCheng/ASAP,ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.,12,True,,MIT,BingqingCheng/ASAP,https://github.com/BingqingCheng/ASAP,2019-08-11 12:45:14,2023-07-25 07:38:32.000000,2023-07-25 07:32:45,758.0,3.0,25.0,7.0,37.0,6.0,18.0,113.0,,,,6.0,,,4.0,4.0,,,,,,,2.0,,,,,,,,,,,,,,,
-79,DMFF,,mliap,https://github.com/deepmodeling/DMFF,DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable..,12,True,,LGPL-3.0,deepmodeling/DMFF,https://github.com/deepmodeling/DMFF,2022-02-14 01:35:50,2023-08-25 09:46:37.000000,2023-02-14 05:57:53,273.0,,27.0,12.0,96.0,9.0,5.0,108.0,2022-12-02 14:41:23,0.2.0,3.0,7.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-80,Librascal,,rep-eng,https://github.com/lab-cosmo/librascal,A scalable and versatile library to generate representations for atomic-scale learning.,12,True,,LGPL-2.1,lab-cosmo/librascal,https://github.com/lab-cosmo/librascal,2018-02-01 08:38:51,2023-06-06 10:01:25.000000,2023-06-06 10:01:20,2930.0,2.0,18.0,19.0,198.0,99.0,131.0,70.0,,,3.0,29.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-81,Neural fingerprint (nfp),,rep-learn,https://github.com/NREL/nfp,Keras layers for end-to-end learning with rdkit and pymatgen.,12,False,,https://github.com/NREL/nfp/blob/master/LICENSE,NREL/nfp,https://github.com/NREL/nfp,2018-11-20 23:55:23,2022-08-29 21:59:14.000000,2022-06-14 22:18:28,143.0,,24.0,7.0,18.0,,6.0,53.0,2022-04-27 17:05:25,0.3.12,13.0,4.0,,,12.0,12.0,,,,,,,2.0,,,,,,,,,,,,,,,
-82,Pacemaker,,mliap,https://cortner.github.io/ACEweb/software/,Python package for fitting atomic cluster expansion (ACE) potentials.,12,True,,https://github.com/ICAMS/python-ace/blob/master/LICENSE.md,ICAMS/python-ace,https://github.com/ICAMS/python-ace,2021-11-19 11:39:54,2023-07-21 17:51:03.000000,2023-07-21 17:50:02,113.0,16.0,9.0,3.0,20.0,6.0,25.0,42.0,2022-10-24 19:59:33,0.2.8,2.0,5.0,python-ace,,,,https://pypi.org/project/python-ace,6.0,6.0,,,,2.0,,,,,,,,,,,,,,,
-83,jarvis-tools-notebooks,,educational,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/.,12,True,,NIST,JARVIS-Materials-Design/jarvis-tools-notebooks,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,2020-06-27 20:22:02,2023-08-19 17:55:57.000000,2023-08-19 17:55:56,499.0,188.0,21.0,3.0,37.0,,,37.0,,,,5.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,
-84,synspace,,generative,https://github.com/whitead/synspace,Synthesis generative model.,12,True,,MIT,whitead/synspace,https://github.com/whitead/synspace,2022-12-28 00:59:14,2023-04-15 22:42:57.000000,2023-04-15 18:04:16,27.0,,3.0,3.0,1.0,2.0,1.0,29.0,2023-04-15 22:42:57,0.3.0,3.0,2.0,synspace,,4.0,4.0,https://pypi.org/project/synspace,332.0,332.0,,,,2.0,,,,,,,,,,,,,,,
-85,Compositionally-Restricted Attention-Based Network (CrabNet),,rep-learn,https://github.com/sparks-baird/CrabNet,Predict materials properties using only the composition information!.,12,True,,MIT,sparks-baird/CrabNet,https://github.com/sparks-baird/CrabNet,2021-09-17 07:58:15,2023-06-19 09:35:52.000000,2023-06-19 09:35:52,427.0,1.0,3.0,1.0,54.0,14.0,2.0,10.0,2023-06-07 01:07:33,2.0.8,5.0,5.0,crabnet,,10.0,10.0,https://pypi.org/project/crabnet,108.0,108.0,,,,2.0,,,,,,,,,,,,,,,
-86,OpenChem,,general-tool,https://github.com/Mariewelt/OpenChem,OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research.,11,False,,MIT,Mariewelt/OpenChem,https://github.com/Mariewelt/OpenChem,2018-07-10 01:27:33,2023-06-06 01:57:19.000000,2022-04-27 19:27:40,444.0,,104.0,37.0,11.0,15.0,2.0,613.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-87,ReLeaSE,,reinforcement-learning,https://github.com/isayev/ReLeaSE,Deep Reinforcement Learning for de-novo Drug Design.,11,False,['drug-discovery'],MIT,isayev/ReLeaSE,https://github.com/isayev/ReLeaSE,2018-04-26 14:50:34,2021-12-08 19:49:36.000000,2021-12-08 19:49:36,160.0,,121.0,19.0,9.0,27.0,8.0,307.0,,,,5.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,
-88,DeepLearningLifeSciences,,educational,https://github.com/deepchem/DeepLearningLifeSciences,Example code from the book Deep Learning for the Life Sciences.,11,False,,MIT,deepchem/DeepLearningLifeSciences,https://github.com/deepchem/DeepLearningLifeSciences,2019-02-05 17:16:18,2021-09-17 05:10:37.000000,2021-09-17 05:10:37,52.0,,133.0,23.0,15.0,10.0,8.0,298.0,2019-10-28 18:46:28,1.0,1.0,10.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-89,Awesome Materials Informatics,,community,https://github.com/tilde-lab/awesome-materials-informatics,Curated list of known efforts in materials informatics = modern materials science.,11,True,topics/materials-informatics,https://github.com/tilde-lab/awesome-materials-informatics#license,tilde-lab/awesome-materials-informatics,https://github.com/tilde-lab/awesome-materials-informatics,2018-02-15 15:14:16,2023-08-21 21:56:00.000000,2023-08-21 21:56:00,130.0,9.0,72.0,15.0,53.0,,8.0,294.0,2023-03-02 19:56:59,2023.03.02,1.0,18.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,
-90,ANI-1,,mliap,https://github.com/isayev/ASE_ANI,ANI-1 neural net potential with python interface (ASE).,11,False,,MIT,isayev/ASE_ANI,https://github.com/isayev/ASE_ANI,2016-12-08 05:09:32,2020-12-14 19:57:50.000000,2020-06-05 22:46:43,111.0,,55.0,36.0,9.0,12.0,20.0,202.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-91,Neural Force Field,,mliap,https://github.com/learningmatter-mit/NeuralForceField,Neural Network Force Field based on PyTorch.,11,True,['pre-trained'],MIT,learningmatter-mit/NeuralForceField,https://github.com/learningmatter-mit/NeuralForceField,2020-10-04 15:17:41,2023-07-25 15:37:02.000000,2023-07-25 15:37:01,122.0,4.0,41.0,8.0,4.0,,16.0,183.0,,,,10.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-92,gptchem,,lm,https://github.com/kjappelbaum/gptchem,Use GPT-3 to solve chemistry problems.,11,True,,MIT,kjappelbaum/gptchem,https://github.com/kjappelbaum/gptchem,2023-01-06 15:34:32,2023-06-22 05:28:27.000000,2023-06-22 05:28:27,108.0,1.0,27.0,5.0,2.0,3.0,2.0,158.0,2023-04-06 19:44:55,0.0.1,1.0,2.0,gptchem,,,,https://pypi.org/project/gptchem,128.0,128.0,,,,2.0,,,,,,,,,,,,,,,
-93,PiNN,,mliap,https://github.com/Teoroo-CMC/PiNN,A Python library for building atomic neural networks.,11,True,,BSD-3-Clause,Teoroo-CMC/PiNN,https://github.com/Teoroo-CMC/PiNN,2019-10-04 08:13:18,2023-08-23 09:38:55.030628,2023-08-18 07:44:37,124.0,11.0,25.0,6.0,1.0,2.0,4.0,94.0,2019-10-09 09:21:30,0.3.0,1.0,2.0,,,,,,,4.0,,,,2.0,teoroo/pinn,https://hub.docker.com/r/teoroo/pinn,2023-08-23 09:38:55.030628,,215.0,,,,,,,,,,
-94,MolSkill,,lm,https://github.com/microsoft/molskill,Learning chemical intuition from humans in the loop. Supporting code.,11,True,"['drug-discovery', 'recommender']",MIT,microsoft/molskill,https://github.com/microsoft/molskill,2023-01-12 13:48:31,2023-08-04 12:22:15.000000,2023-06-13 09:58:31,80.0,3.0,5.0,8.0,8.0,2.0,3.0,77.0,2023-08-04 12:22:15,1.2b,5.0,4.0,,msr-ai4science/molskill,,,,,14.0,https://anaconda.org/msr-ai4science/molskill,2023-06-18 17:27:43.196,89.0,2.0,,,,,,,,,,,,,,,
-95,nlcc,,lm,https://github.com/whitead/nlcc,Natural language computational chemistry command line interface.,11,True,['single-paper'],MIT,whitead/nlcc,https://github.com/whitead/nlcc,2021-08-19 18:23:52,2023-02-04 03:07:56.000000,2023-02-04 03:06:33,144.0,,6.0,4.0,1.0,,9.0,41.0,2023-02-04 03:07:56,0.6.0,10.0,3.0,nlcc,,,,https://pypi.org/project/nlcc,49.0,49.0,,,,2.0,,,,,,,,,,,,,,,
-96,SIMPLE-NN,,mliap,https://github.com/MDIL-SNU/SIMPLE-NN,SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network).,11,False,,GPL-3.0,MDIL-SNU/SIMPLE-NN,https://github.com/MDIL-SNU/SIMPLE-NN,2018-03-26 23:53:35,2022-01-27 05:04:05.000000,2022-01-27 05:04:05,586.0,,18.0,12.0,91.0,4.0,26.0,41.0,2021-09-23 01:41:42,1.1.1,9.0,4.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-97,OpenKIM,,datasets,https://openkim.org/,"The Open Knowledgebase of Interatomic Models (OpenKIM) aims to be an online resource for standardized testing, long-..",11,False,"['knowledge-base', 'pre-trained']",LGPL-2.1,openkim/kim-api,https://github.com/openkim/kim-api,2014-07-28 21:21:08,2023-08-16 00:09:44.000000,2022-03-17 23:01:36,2371.0,,19.0,11.0,55.0,17.0,18.0,29.0,,,,23.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,
-98,SchNetPack G-SchNet,,generative,https://github.com/atomistic-machine-learning/schnetpack-gschnet,G-SchNet extension for SchNetPack.,11,True,,MIT,atomistic-machine-learning/schnetpack-gschnet,https://github.com/atomistic-machine-learning/schnetpack-gschnet,2022-04-21 12:34:13,2023-06-01 14:11:50.000000,2023-06-01 14:11:46,116.0,1.0,3.0,4.0,,,8.0,26.0,2023-04-25 14:09:07,1.0.0,2.0,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-99,Rascaline,,rep-eng,https://github.com/Luthaf/rascaline,Computing representations for atomistic machine learning.,11,True,"['lang-rust', 'lang-cpp']",BSD-3-Clause,Luthaf/rascaline,https://github.com/Luthaf/rascaline,2020-09-24 14:28:34,2023-08-24 16:57:37.000000,2023-08-22 14:40:47,446.0,58.0,11.0,6.0,186.0,18.0,18.0,13.0,,,,10.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-100,ACEfit,,mliap,https://github.com/ACEsuit/ACEfit.jl,,11,False,['lang-julia'],MIT,ACEsuit/ACEfit.jl,https://github.com/ACEsuit/ACEfit.jl,2022-01-01 00:09:17,2023-08-18 16:52:10.000000,2023-08-18 16:46:11,210.0,36.0,3.0,3.0,14.0,21.0,32.0,4.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-101,SE(3)-Transformers,,rep-learn,https://github.com/FabianFuchsML/se3-transformer-public,code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503.,10,False,['single-paper'],MIT,FabianFuchsML/se3-transformer-public,https://github.com/FabianFuchsML/se3-transformer-public,2020-08-31 10:36:57,2023-07-10 05:13:25.000000,2021-11-18 09:11:56,63.0,,62.0,15.0,5.0,9.0,17.0,408.0,,,,2.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-102,GDC,,rep-learn,https://github.com/gasteigerjo/gdc,"Graph Diffusion Convolution, as proposed in Diffusion Improves Graph Learning (NeurIPS 2019).",10,True,['generative'],MIT,gasteigerjo/gdc,https://github.com/gasteigerjo/gdc,2019-10-26 16:05:11,2023-04-26 14:22:40.000000,2023-04-26 14:22:40,28.0,,38.0,3.0,1.0,,10.0,219.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-103,DeePKS-kit,,ml-dft,https://github.com/deepmodeling/deepks-kit,a package for developing machine learning-based chemically accurate energy and density functional models.,10,True,,LGPL-3.0,deepmodeling/deepks-kit,https://github.com/deepmodeling/deepks-kit,2020-07-29 03:27:50,2023-08-24 00:29:24.000000,2023-04-01 01:14:46,380.0,,29.0,12.0,39.0,1.0,9.0,93.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-104,flare++,,active-learning,https://github.com/mir-group/flare_pp,A many-body extension of the FLARE code.,10,False,"['lang-cpp', 'mliap']",MIT,mir-group/flare_pp,https://github.com/mir-group/flare_pp,2019-11-20 22:46:32,2022-02-27 21:05:09.000000,2022-02-24 19:00:50,989.0,,6.0,6.0,28.0,8.0,17.0,34.0,,,,10.0,flare_pp,,,,https://pypi.org/project/flare_pp,129.0,129.0,,,,2.0,,,,,,,,,,,,,,,
-105,cmlkit,,rep-eng,https://github.com/sirmarcel/cmlkit,tools for machine learning in condensed matter physics and quantum chemistry.,10,False,['benchmarking'],MIT,sirmarcel/cmlkit,https://github.com/sirmarcel/cmlkit,2018-05-31 07:56:52,2022-04-01 00:39:14.000000,2022-03-25 22:27:04,526.0,,6.0,3.0,1.0,6.0,2.0,29.0,,,,,cmlkit,,4.0,4.0,https://pypi.org/project/cmlkit,35.0,35.0,,,,2.0,,,,,,,,,,,,,,,
-106,NeuralXC,,ml-dft,https://github.com/semodi/neuralxc,Implementation of a machine learned density functional.,10,False,,BSD-3-Clause,semodi/neuralxc,https://github.com/semodi/neuralxc,2019-03-14 18:13:40,2022-11-30 11:39:22.000000,2021-07-05 21:36:23,337.0,,8.0,5.0,9.0,5.0,5.0,28.0,,,3.0,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-107,Finetuna,,active-learning,https://github.com/ulissigroup/finetuna,Active Learning for Machine Learning Potentials.,10,True,,MIT,ulissigroup/finetuna,https://github.com/ulissigroup/finetuna,2020-09-22 14:39:52,2023-03-15 18:08:52.000000,2023-02-13 20:10:48,1196.0,,6.0,3.0,37.0,3.0,14.0,25.0,,,,11.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-108,graphite,,rep-learn,https://github.com/LLNL/graphite,A repository for implementing graph network models based on atomic structures.,10,True,,MIT,llnl/graphite,https://github.com/LLNL/graphite,2022-06-27 19:15:27,2023-08-16 20:32:27.000000,2023-08-16 20:32:27,22.0,14.0,4.0,6.0,4.0,1.0,,21.0,,,,2.0,,,6.0,6.0,,,,,,,2.0,,,,,,,,,,,,,,,
-109,ACE1.jl,,mliap,https://acesuit.github.io/,Atomic Cluster Expansion for Modelling Invariant Atomic Properties.,10,True,['lang-julia'],https://github.com/ACEsuit/ACE1.jl/blob/main/ASL.md,ACEsuit/ACE1.jl,https://github.com/ACEsuit/ACE1.jl,2022-01-14 19:52:49,2023-08-22 07:18:10.000000,2023-08-22 07:18:07,546.0,22.0,4.0,5.0,28.0,21.0,24.0,18.0,,,,7.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-110,NNsforMD,,mliap,https://github.com/aimat-lab/NNsForMD,"Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings.",10,True,,MIT,aimat-lab/NNsForMD,https://github.com/aimat-lab/NNsForMD,2020-08-31 11:14:18,2022-11-10 13:04:49.000000,2022-11-10 13:04:45,265.0,,3.0,3.0,,,,9.0,2022-04-12 15:10:32,2.0.0,5.0,2.0,pyNNsMD,,,,https://pypi.org/project/pyNNsMD,33.0,33.0,,,,3.0,,,,,,,,,,,,,,,
-111,ML4pXRDs,,rep-learn,https://github.com/aimat-lab/ML4pXRDs,Contains code to train neural networks based on simulated powder XRDs from synthetic crystals.,10,True,"['xrd', 'single-paper']",MIT,aimat-lab/ML4pXRDs,https://github.com/aimat-lab/ML4pXRDs,2022-12-01 16:24:29,2023-07-14 08:17:06.000000,2023-07-14 08:17:04,1320.0,21.0,1.0,3.0,,,,,2023-03-22 11:04:31,1.0,1.0,,,,,,,,0.0,,,,2.0,,,,,,2.0,,,,,,,,,
-112,molecularGNN_smiles,,rep-learn,https://github.com/masashitsubaki/molecularGNN_smiles,"The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius..",9,False,,Apache-2.0,masashitsubaki/molecularGNN_smiles,https://github.com/masashitsubaki/molecularGNN_smiles,2018-11-06 00:25:26,2020-11-28 02:04:45.000000,2020-11-28 02:04:45,79.0,,68.0,6.0,,6.0,1.0,246.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-113,DimeNet,,mliap,https://github.com/gasteigerjo/dimenet,"DimeNet and DimeNet++ models, as proposed in Directional Message Passing for Molecular Graphs (ICLR 2020) and Fast and..",9,True,,https://github.com/gasteigerjo/dimenet/blob/master/LICENSE.md,gasteigerjo/dimenet,https://github.com/gasteigerjo/dimenet,2020-02-14 12:40:15,2023-08-01 19:36:15.000000,2023-08-01 19:36:15,102.0,2.0,52.0,5.0,,1.0,29.0,244.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-114,Allegro,,mliap,https://github.com/mir-group/allegro,Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic..,9,True,,MIT,mir-group/allegro,https://github.com/mir-group/allegro,2022-02-06 23:50:40,2023-05-08 21:16:45.000000,2023-05-08 21:16:45,38.0,,31.0,16.0,2.0,6.0,12.0,221.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-115,SchNet,,mliap,https://github.com/atomistic-machine-learning/SchNet,SchNet - a deep learning architecture for quantum chemistry.,9,False,,MIT,atomistic-machine-learning/SchNet,https://github.com/atomistic-machine-learning/SchNet,2017-10-03 11:52:20,2018-09-04 08:42:35.000000,2018-09-04 08:42:34,53.0,,57.0,16.0,,1.0,2.0,176.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-116,QDF for molecule,,ml-esm,https://github.com/masashitsubaki/QuantumDeepField_molecule,"Quantum deep field: data-driven wave function, electron density generation, and energy prediction and extrapolation..",9,False,,MIT,masashitsubaki/QuantumDeepField_molecule,https://github.com/masashitsubaki/QuantumDeepField_molecule,2020-11-11 01:06:09,2021-02-20 03:46:18.000000,2021-02-20 03:46:09,20.0,,38.0,3.0,,,3.0,167.0,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,
-117,ACE.jl,,mliap,https://github.com/ACEsuit/ACE.jl,Parameterisation of Equivariant Properties of Particle Systems.,9,True,['lang-julia'],https://github.com/ACEsuit/ACE.jl/blob/main/license/mit.md,ACEsuit/ACE.jl,https://github.com/ACEsuit/ACE.jl,2019-11-30 16:22:51,2023-06-09 21:31:30.000000,2023-06-09 21:29:10,912.0,1.0,15.0,8.0,65.0,23.0,58.0,61.0,,,,12.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-118,GATGNN: Global Attention Graph Neural Network,,rep-learn,https://github.com/superlouis/GATGNN,Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials..,9,True,,MIT,superlouis/GATGNN,https://github.com/superlouis/GATGNN,2020-06-21 03:27:36,2022-10-03 21:57:33.000000,2022-10-03 21:57:33,99.0,,18.0,8.0,,3.0,3.0,60.0,2021-04-05 06:49:29,0.2,2.0,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-119,DeepErwin,,ml-wft,https://github.com/mdsunivie/deeperwin,DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions..,9,True,,https://github.com/mdsunivie/deeperwin/blob/master/LICENSE,mdsunivie/deeperwin,https://github.com/mdsunivie/deeperwin,2021-06-14 15:18:32,2023-07-17 13:48:55.000000,2023-07-17 13:39:49,55.0,4.0,5.0,3.0,3.0,1.0,7.0,33.0,2022-07-18 10:18:25,arxiv_2105.08351v2,2.0,6.0,deeperwin,,,,https://pypi.org/project/deeperwin,8.0,8.0,,,,3.0,,,,,,,,,,,,,,,
-120,GAP,,mliap,https://libatoms.github.io/,Gaussian Approximation Potential (GAP).,9,True,,https://github.com/libAtoms/GAP/blob/main/LICENSE.md,libAtoms/GAP,https://github.com/libAtoms/GAP,2021-03-22 14:48:56,2023-06-08 10:42:38.000000,2023-06-08 10:42:38,199.0,4.0,20.0,10.0,63.0,,,30.0,,,,12.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-121,ALF,,mliap,https://github.com/lanl/ALF,A framework for performing active learning for training machine-learned interatomic potentials.,9,True,['active-learning'],https://github.com/lanl/ALF/blob/main/LICENSE,lanl/alf,https://github.com/lanl/ALF,2023-01-04 23:13:24,2023-08-23 13:36:54.000000,2023-08-04 15:53:59,139.0,61.0,7.0,6.0,23.0,,,19.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-122,CGAT,,rep-learn,https://github.com/hyllios/CGAT,Crystal graph attention neural networks for materials prediction.,9,True,,MIT,hyllios/CGAT,https://github.com/hyllios/CGAT,2021-03-28 09:51:15,2023-07-18 12:04:35.000000,2023-01-10 22:31:07,153.0,,7.0,3.0,1.0,,1.0,13.0,2023-07-18 12:04:35,0.1,1.0,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-123,SALTED,,ml-dft,https://github.com/andreagrisafi/SALTED,Symmetry-Adapted Learning of Three-dimensional Electron Densities.,9,True,,GPL-3.0,andreagrisafi/SALTED,https://github.com/andreagrisafi/SALTED,2020-01-22 10:24:29,2023-08-23 15:04:49.000000,2023-08-22 12:13:16,156.0,17.0,2.0,1.0,5.0,1.0,,12.0,2023-04-10 16:25:44,2.0.0,1.0,7.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-124,ACE1Pack.jl,,mliap,https://github.com/ACEsuit/ACE1pack.jl,"Provides convenience functionality for the usage of ACE1.jl, ACEfit.jl, JuLIP.jl for fitting interatomic potentials..",9,True,['lang-julia'],MIT,ACEsuit/ACE1pack.jl,https://github.com/ACEsuit/ACE1pack.jl,2023-08-21 16:25:00,2023-08-21 16:30:19.000000,2023-08-21 15:48:54,547.0,50.0,,,,,,,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,https://acesuit.github.io/ACE1pack.jl,,,
-125,RDKit Tutorials,,educational,https://github.com/rdkit/rdkit-tutorials,Tutorials to learn how to work with the RDKit.,8,True,,https://github.com/rdkit/rdkit-tutorials/blob/master/LICENSE,rdkit/rdkit-tutorials,https://github.com/rdkit/rdkit-tutorials,2016-10-07 03:34:01,2023-03-19 13:36:55.000000,2023-03-19 13:36:55,68.0,,65.0,16.0,7.0,3.0,1.0,199.0,,,,5.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-126,The Collection of Database and Dataset Resources in Materials Science,,community,https://github.com/sedaoturak/data-resources-for-materials-science,"A list of databases, datasets and books/handbooks where you can find materials properties for machine learning..",8,True,['datasets'],,sedaoturak/data-resources-for-materials-science,https://github.com/sedaoturak/data-resources-for-materials-science,2021-02-20 06:38:45,2023-06-27 13:32:27.000000,2023-06-27 13:32:27,26.0,3.0,25.0,9.0,1.0,,,155.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,
-127,GemNet,,mliap,https://github.com/TUM-DAML/gemnet_pytorch,"GemNet model in PyTorch, as proposed in GemNet: Universal Directional Graph Neural Networks for Molecules (NeurIPS..",8,True,,https://github.com/TUM-DAML/gemnet_pytorch/blob/master/LICENSE,TUM-DAML/gemnet_pytorch,https://github.com/TUM-DAML/gemnet_pytorch,2021-10-11 07:30:36,2023-04-26 14:20:12.000000,2023-04-26 14:20:12,36.0,,25.0,4.0,1.0,,13.0,147.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-128,G-SchNet,,generative,https://github.com/atomistic-machine-learning/G-SchNet,G-SchNet - a generative model for 3d molecular structures.,8,True,,MIT,atomistic-machine-learning/G-SchNet,https://github.com/atomistic-machine-learning/G-SchNet,2019-10-21 13:48:59,2023-03-24 12:05:41.000000,2023-03-24 12:05:41,64.0,,22.0,6.0,,,10.0,115.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-129,ANI-1 Dataset,,datasets,https://github.com/isayev/ANI1_dataset,A data set of 20 million calculated off-equilibrium conformations for organic molecules.,8,True,,MIT,isayev/ANI1_dataset,https://github.com/isayev/ANI1_dataset,2017-08-07 20:08:46,2022-08-08 15:56:17.000000,2022-08-08 15:56:17,25.0,,19.0,12.0,2.0,6.0,3.0,87.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-130,MoleculeNet Leaderboard,,datasets,https://github.com/deepchem/moleculenet,,8,False,['benchmarking'],MIT,deepchem/moleculenet,https://github.com/deepchem/moleculenet,2020-02-24 18:14:05,2021-04-29 19:51:06.000000,2021-04-29 19:51:06,78.0,,18.0,5.0,15.0,22.0,5.0,73.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-131,matsciml,,rep-learn,https://github.com/IntelLabs/matsciml,Open MatSci ML Toolkit is a single framework for prototyping and scaling out deep learning models for materials..,8,True,['workflows'],MIT,IntelLabs/matsciml,https://github.com/IntelLabs/matsciml,2022-09-13 20:27:28,2023-08-11 01:25:27.000000,2023-07-11 21:56:44,22.0,1.0,8.0,4.0,35.0,4.0,5.0,48.0,,,1.0,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-132,cG-SchNet,,generative,https://github.com/atomistic-machine-learning/cG-SchNet,cG-SchNet - a conditional generative neural network for 3d molecular structures.,8,True,,MIT,atomistic-machine-learning/cG-SchNet,https://github.com/atomistic-machine-learning/cG-SchNet,2021-12-02 15:35:18,2023-03-24 12:09:56.000000,2023-03-24 12:09:56,28.0,,13.0,4.0,,,3.0,43.0,2022-02-21 13:36:41,1.0,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-133,hippynn,,rep-learn,https://github.com/lanl/hippynn,python library for atomistic machine learning.,8,True,['workflows'],https://github.com/lanl/hippynn/blob/main/LICENSE.txt,lanl/hippynn,https://github.com/lanl/hippynn,2021-11-17 00:45:13,2023-08-05 17:41:55.000000,2023-08-05 17:38:53,101.0,7.0,18.0,6.0,37.0,3.0,1.0,41.0,,,2.0,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-134,Sketchmap,,unsupervised,https://github.com/lab-cosmo/sketchmap,Suite of programs to perform non-linear dimensionality reduction -- sketch-map in particular.,8,True,['lang-cpp'],GPL-3.0,lab-cosmo/sketchmap,https://github.com/lab-cosmo/sketchmap,2014-05-20 09:33:32,2023-05-24 22:56:06.000000,2023-05-24 22:47:50,64.0,,10.0,29.0,1.0,3.0,5.0,39.0,,,,8.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-135,DeeperGATGNN,,rep-learn,https://github.com/usccolumbia/deeperGATGNN,Scalable graph neural networks for materials property prediction.,8,True,,MIT,usccolumbia/deeperGATGNN,https://github.com/usccolumbia/deeperGATGNN,2021-09-29 17:31:02,2023-04-19 18:59:51.000000,2023-04-19 18:59:31,24.0,,7.0,3.0,1.0,,7.0,32.0,2022-03-08 02:14:28,1.0,1.0,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-136,SNAP,,mliap,https://github.com/materialsvirtuallab/snap,Repository for spectral neighbor analysis potential (SNAP) model development.,8,False,,BSD-3-Clause,materialsvirtuallab/snap,https://github.com/materialsvirtuallab/snap,2017-06-26 21:56:00,2020-06-30 05:20:37.000000,2020-06-30 05:20:37,38.0,,16.0,10.0,1.0,1.0,3.0,32.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-137,SIMPLE-NN v2,,mliap,https://github.com/MDIL-SNU/SIMPLE-NN_v2,,8,False,,GPL-3.0,MDIL-SNU/SIMPLE-NN_v2,https://github.com/MDIL-SNU/SIMPLE-NN_v2,2021-03-02 09:36:49,2023-08-25 06:33:45.000000,2023-08-25 06:33:45,498.0,6.0,13.0,5.0,86.0,3.0,7.0,24.0,,,,12.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-138,Atomistic Adversarial Attacks,,mliap,https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks,Code for performing adversarial attacks on atomistic systems using NN potentials.,8,True,['probabilistic'],MIT,learningmatter-mit/Atomistic-Adversarial-Attacks,https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks,2021-03-28 17:39:52,2022-10-03 16:19:31.000000,2022-10-03 16:19:29,33.0,,6.0,5.0,1.0,,1.0,24.0,2021-07-19 18:09:36,1.0.1,1.0,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-139,AdsorbML,,rep-learn,https://github.com/Open-Catalyst-Project/AdsorbML,,8,True,"['surface-science', 'single-paper']",MIT,Open-Catalyst-Project/AdsorbML,https://github.com/Open-Catalyst-Project/AdsorbML,2022-11-30 01:38:20,2023-07-31 16:28:14.000000,2023-07-31 16:28:09,56.0,16.0,4.0,6.0,10.0,,1.0,17.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-140,iam-notebooks,,educational,https://github.com/ceriottm/iam-notebooks,Jupyter notebooks for the lectures of the Introduction to Atomistic Modeling.,8,True,,Apache-2.0,ceriottm/iam-notebooks,https://github.com/ceriottm/iam-notebooks,2020-11-23 21:27:41,2023-08-07 23:02:34.000000,2023-08-07 23:02:34,228.0,1.0,4.0,4.0,7.0,3.0,,16.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,-1.0,,,,,,,
-141,UVVisML,,rep-learn,https://github.com/learningmatter-mit/uvvisml,Predict optical properties of molecules with machine learning.,8,True,"['optical-properties', 'single-paper', 'probabilistic']",MIT,learningmatter-mit/uvvisml,https://github.com/learningmatter-mit/uvvisml,2021-10-13 05:58:48,2023-05-26 22:35:14.000000,2023-05-26 22:35:14,17.0,,4.0,4.0,1.0,,,11.0,2022-02-06 18:14:14,0.0.2,2.0,,,,,,,,,,,,3.0,,,,,,,,1.0,,,,,,,
-142,bVAE-IM,,generative,https://github.com/tsudalab/bVAE-IM,Implementation of Chemical Design with GPU-based Ising Machine.,8,True,"['qml', 'single-paper']",MIT,tsudalab/bVAE-IM,https://github.com/tsudalab/bVAE-IM,2023-03-01 08:26:56,2023-07-11 04:39:24.000000,2023-07-11 04:39:24,39.0,6.0,2.0,8.0,,,,8.0,2023-03-01 14:26:13,1.0.0,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-143,MAChINE,,educational,https://github.com/aimat-lab/MAChINE,Client-Server Web App to introduce usage of ML in materials science to beginners.,8,False,,MIT,aimat-lab/MAChINE,https://github.com/aimat-lab/MAChINE,2023-04-17 14:29:06,2023-08-24 14:39:40.000000,2023-08-08 11:38:34,984.0,64.0,,,4.0,15.0,16.0,1.0,,,,7.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-144,"Data Handling, DoE and Statistical Analysis for Material Chemists",,educational,https://github.com/Teoroo-CMC/DoE_Course_Material,"Notebooks for workshops of DoE course, hosted by the Computational Materials Chemistry group at Uppsala University.",8,True,,GPL-3.0,Teoroo-CMC/DoE_Course_Material,https://github.com/Teoroo-CMC/DoE_Course_Material,2023-05-22 08:11:41,2023-06-26 12:48:17.000000,2023-06-26 12:48:15,157.0,113.0,12.0,2.0,1.0,,,,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-145,Equiformer,,rep-learn,https://github.com/atomicarchitects/equiformer,[ICLR23 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs.,7,True,,MIT,atomicarchitects/equiformer,https://github.com/atomicarchitects/equiformer,2023-02-28 00:21:30,2023-06-21 08:04:30.000000,2023-06-21 08:03:53,3.0,2.0,22.0,5.0,1.0,5.0,7.0,119.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-146,A Highly Opinionated List of Open-Source Materials Informatics Resources,,community,https://github.com/ncfrey/resources,A Highly Opinionated List of Open Source Materials Informatics Resources.,7,False,,MIT,ncfrey/resources,https://github.com/ncfrey/resources,2020-11-17 23:47:07,2022-02-18 13:37:51.000000,2022-02-18 13:37:51,8.0,,19.0,9.0,,,,93.0,,,,,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,
-147,DTNN,,rep-learn,https://github.com/atomistic-machine-learning/dtnn,Deep Tensor Neural Network.,7,False,,MIT,atomistic-machine-learning/dtnn,https://github.com/atomistic-machine-learning/dtnn,2017-03-10 14:40:05,2017-07-11 08:26:15.000000,2017-07-11 08:25:39,9.0,,30.0,15.0,,,3.0,77.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-148,AIMNet,,mliap,https://github.com/aiqm/aimnet,Atoms In Molecules Neural Network Potential.,7,False,['single-paper'],MIT,aiqm/aimnet,https://github.com/aiqm/aimnet,2018-09-26 17:28:37,2019-11-21 23:49:01.000000,2019-11-21 23:49:00,7.0,,20.0,10.0,2.0,3.0,,75.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-149,JAXChem,,general-tool,https://github.com/deepchem/jaxchem,JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling.,7,False,,MIT,deepchem/jaxchem,https://github.com/deepchem/jaxchem,2020-05-11 18:54:41,2020-07-15 05:02:21.000000,2020-07-15 04:55:41,96.0,,9.0,7.0,13.0,1.0,1.0,74.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-150,PhysNet,,mliap,https://github.com/MMunibas/PhysNet,Code for training PhysNet models.,7,False,['electrostatics'],MIT,MMunibas/PhysNet,https://github.com/MMunibas/PhysNet,2019-03-28 09:05:22,2022-10-16 17:45:42.000000,2020-12-07 11:09:20,4.0,,26.0,9.0,1.0,3.0,,74.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-151,Cormorant,,rep-learn,https://github.com/risilab/cormorant,Codebase for Cormorant Neural Networks.,7,False,,https://github.com/risilab/cormorant/blob/master/LICENSE,risilab/cormorant,https://github.com/risilab/cormorant,2019-10-27 18:22:07,2022-05-11 12:49:05.000000,2020-03-11 15:25:51,160.0,,10.0,6.0,1.0,3.0,,53.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-152,uncertainty_benchmarking,,general-tool,https://github.com/ulissigroup/uncertainty_benchmarking,Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions.,7,False,"['benchmarking', 'probabilistic']",,ulissigroup/uncertainty_benchmarking,https://github.com/ulissigroup/uncertainty_benchmarking,2019-08-28 19:39:28,2021-06-07 23:29:39.000000,2021-06-07 23:27:19,265.0,,6.0,6.0,1.0,,,34.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-153,MACE-Jax,,mliap,https://github.com/ACEsuit/mace-jax,Equivariant machine learning interatomic potentials in JAX.,7,True,,MIT,ACEsuit/mace-jax,https://github.com/ACEsuit/mace-jax,2023-02-06 12:10:16,2023-07-20 18:07:51.000000,2023-07-20 18:08:42,206.0,8.0,1.0,9.0,1.0,,1.0,33.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-154,torchchem,,general-tool,https://github.com/deepchem/torchchem,An experimental repo for experimenting with PyTorch models.,7,False,,MIT,deepchem/torchchem,https://github.com/deepchem/torchchem,2020-03-07 17:06:44,2023-03-24 23:13:19.000000,2020-05-01 20:12:23,49.0,,14.0,8.0,27.0,2.0,1.0,33.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-155,BOSS,,materials-discovery,https://gitlab.com/cest-group/boss,Bayesian Optimization Structure Search (BOSS).,7,False,['probabilistic'],,,,2020-02-12 08:48:33,2020-02-12 08:48:33.000000,,,,9.0,,,4.0,21.0,18.0,,,13.0,,aalto-boss,,,,https://pypi.org/project/aalto-boss,194.0,194.0,,,,2.0,,,,,,,,1.0,,,,,cest-group/boss,https://gitlab.com/cest-group/boss,
-156,GElib,,math,https://github.com/risi-kondor/GElib,C++/CUDA library for SO(3) equivariant operations.,7,True,['lang-cpp'],MPL-2.0,risi-kondor/GElib,https://github.com/risi-kondor/GElib,2021-08-24 20:56:40,2023-08-02 00:27:18.000000,2023-07-23 04:10:48,562.0,20.0,2.0,2.0,2.0,3.0,1.0,16.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-157,CBFV,,rep-eng,https://github.com/Kaaiian/CBFV,Tool to quickly create a composition-based feature vector.,7,False,,,kaaiian/CBFV,https://github.com/Kaaiian/CBFV,2019-09-05 23:07:46,2022-03-30 05:47:53.000000,2021-10-24 17:10:17,49.0,,5.0,4.0,7.0,5.0,5.0,13.0,,,,3.0,CBFV,,3.0,3.0,https://pypi.org/project/CBFV,76.0,76.0,,,,2.0,,,,,,,,,,,,,,,
-158,Libnxc,,ml-dft,https://github.com/semodi/libnxc,A library for using machine-learned exchange-correlation functionals for density-functional theory.,7,False,"['lang-cpp', 'lang-fortran']",MPL-2.0,semodi/libnxc/,https://github.com/semodi/libnxc,2020-07-01 18:21:50,2021-09-18 14:53:52.000000,2021-08-14 16:26:32,100.0,,4.0,2.0,3.0,13.0,3.0,13.0,,,2.0,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-159,BenchML,,rep-eng,https://github.com/capoe/benchml,ML benchmarking and pipeling framework.,7,True,['benchmarking'],Apache-2.0,capoe/benchml,https://github.com/capoe/benchml,2020-04-28 13:26:29,2023-05-24 15:13:06.000000,2023-05-24 15:04:57,341.0,,2.0,5.0,7.0,3.0,10.0,13.0,,,,9.0,benchml,,,,https://pypi.org/project/benchml,12.0,12.0,,,,2.0,,,,,,,,,,,,,,,
-160,ACEhamiltonians,,ml-dft,https://github.com/ACEsuit/ACEhamiltonians.jl,"Provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-..",7,True,['lang-julia'],MIT,ACEsuit/ACEhamiltonians.jl,https://github.com/ACEsuit/ACEhamiltonians.jl,2022-01-17 20:54:22,2023-04-12 15:11:09.000000,2023-04-12 15:04:14,33.0,,4.0,4.0,41.0,1.0,3.0,7.0,2022-05-20 17:07:42,arXiv.2111.13736,1.0,4.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-161,Equisolve,,general-tool,https://github.com/lab-cosmo/equisolve,A package tasked with taking equistore objects and computing machine learning models using them.,7,False,['mliap'],BSD-3-Clause,lab-cosmo/equisolve,https://github.com/lab-cosmo/equisolve,2022-10-04 15:29:19,2023-08-09 11:37:22.000000,2023-07-10 13:27:31,37.0,5.0,2.0,14.0,37.0,17.0,4.0,4.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-162,pyLODE,,rep-eng,https://github.com/ceriottm/lode,Pythonic implementation of LOng Distance Equivariants.,7,False,['electrostatics'],Apache-2.0,ceriottm/lode,https://github.com/ceriottm/lode,2022-01-19 17:01:38,2023-07-05 09:57:29.000000,2023-07-05 09:57:14,241.0,3.0,1.0,3.0,,1.0,,2.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-163,BestPractices,,educational,https://github.com/anthony-wang/BestPractices,Things that you should (and should not) do in your Materials Informatics research.,6,False,,MIT,anthony-wang/BestPractices,https://github.com/anthony-wang/BestPractices,2020-05-05 19:41:25,2023-03-10 21:27:15.000000,2021-02-18 08:56:47,15.0,,63.0,7.0,7.0,5.0,2.0,134.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-164,Applied AI for Materials,,educational,https://github.com/WardLT/applied-ai-for-materials,Course materials for Applied AI for Materials Science and Engineering.,6,False,,,WardLT/applied-ai-for-materials,https://github.com/WardLT/applied-ai-for-materials,2020-10-12 19:39:06,2022-03-12 02:26:58.000000,2022-03-12 02:26:41,107.0,,28.0,4.0,13.0,5.0,,46.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-165,ANI-1x Datasets,,datasets,https://github.com/aiqm/ANI1x_datasets,"The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules.",6,False,,MIT,aiqm/ANI1x_datasets,https://github.com/aiqm/ANI1x_datasets,2019-09-17 18:19:28,2022-04-11 17:25:55.000000,2022-04-11 17:25:55,12.0,,5.0,6.0,,2.0,3.0,45.0,,,,2.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-166,COMP6 Benchmark dataset,,datasets,https://github.com/isayev/COMP6,COMP6 Benchmark dataset for ML potentials.,6,False,,MIT,isayev/COMP6,https://github.com/isayev/COMP6,2017-12-29 16:58:35,2018-07-09 23:56:35.000000,2018-07-09 23:56:34,27.0,,4.0,5.0,,2.0,1.0,35.0,,,,2.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-167,DeepDFT,,ml-dft,https://github.com/peterbjorgensen/DeepDFT,Official implementation of DeepDFT model.,6,True,,MIT,peterbjorgensen/DeepDFT,https://github.com/peterbjorgensen/DeepDFT,2020-11-03 11:51:15,2023-02-28 15:37:49.000000,2023-02-28 15:37:37,128.0,,6.0,1.0,,,3.0,35.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-168,milad,,rep-eng,https://github.com/muhrin/milad,Moment Invariants Local Atomic Descriptor.,6,True,['generative'],GPL-3.0,muhrin/milad,https://github.com/muhrin/milad,2020-04-23 09:14:24,2022-12-03 10:40:05.000000,2022-12-03 10:39:59,110.0,,1.0,4.0,,,,27.0,,,,,,,1.0,1.0,,,,,,,3.0,,,,,,,,,,,,,,,
-169,MACE-Layer,,rep-learn,https://github.com/ACEsuit/mace-layer,Higher order equivariant graph neural networks for 3D point clouds.,6,True,,MIT,ACEsuit/mace-layer,https://github.com/ACEsuit/mace-layer,2022-11-09 17:03:41,2023-06-27 15:32:49.000000,2023-06-06 10:09:58,19.0,1.0,3.0,6.0,2.0,1.0,,25.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-170,PACE,,md,https://github.com/ICAMS/lammps-user-pace,"The LAMMPS MLIAP `pair_style pace`, aka Atomic Cluster Expansion (ACE), aka ML-PACE,..",6,True,,https://github.com/ICAMS/lammps-user-pace/blob/main/LICENSE,ICAMS/lammps-user-pace,https://github.com/ICAMS/lammps-user-pace,2021-02-25 10:04:48,2023-05-25 22:16:12.000000,2023-01-31 19:53:46,46.0,,10.0,5.0,11.0,1.0,5.0,20.0,,,1.0,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-171,GLAMOUR,,rep-learn,https://github.com/learningmatter-mit/GLAMOUR,Graph Learning over Macromolecule Representations.,6,True,['single-paper'],MIT,learningmatter-mit/GLAMOUR,https://github.com/learningmatter-mit/GLAMOUR,2021-08-20 18:16:40,2022-12-31 17:56:21.000000,2022-12-31 17:56:21,14.0,,5.0,3.0,,,8.0,18.0,2021-08-23 18:58:52,0.1,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-172,SA-GPR,,rep-eng,https://github.com/dilkins/TENSOAP,Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR).,6,True,['lang-c'],LGPL-3.0,dilkins/TENSOAP,https://github.com/dilkins/TENSOAP,2020-05-04 14:19:01,2023-04-07 09:58:08.000000,2022-09-29 09:30:45,25.0,,9.0,3.0,10.0,2.0,5.0,14.0,2020-12-17 16:51:47,2020.0,1.0,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-173,testing-framework,,mliap,https://github.com/libAtoms/testing-framework,The purpose of this repository is to aid the testing of a large number of interatomic potentials for a variety of..,6,False,['benchmarking'],,libAtoms/testing-framework,https://github.com/libAtoms/testing-framework,2020-03-04 11:43:15,2022-02-10 17:23:46.000000,2022-02-10 17:23:46,225.0,,6.0,16.0,10.0,5.0,3.0,11.0,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-174,NICE,,rep-eng,https://github.com/lab-cosmo/nice,NICE (N-body Iteratively Contracted Equivariants) is a set of tools designed for the calculation of invariant and..,6,True,,MIT,lab-cosmo/nice,https://github.com/lab-cosmo/nice,2020-07-03 08:47:41,2023-05-01 09:22:21.000000,2023-05-01 09:21:56,231.0,,2.0,6.0,7.0,2.0,1.0,10.0,,,1.0,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-175,CatGym,,reinforcement-learning,https://github.com/ulissigroup/catgym,Surface segregation using Deep Reinforcement Learning.,6,False,,GPL,ulissigroup/catgym,https://github.com/ulissigroup/catgym,2019-08-06 19:25:27,2021-08-30 17:05:36.000000,2021-08-30 17:05:32,162.0,,2.0,4.0,,1.0,,10.0,,,,7.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-176,fplib,,rep-eng,https://github.com/zhuligs/fplib,a fingerprint library.,6,False,"['lang-c', 'single-paper']",MIT,zhuligs/fplib,https://github.com/zhuligs/fplib,2015-09-07 08:18:27,2022-02-09 05:31:21.000000,2022-02-09 05:31:12,37.0,,2.0,3.0,,,3.0,7.0,2021-02-03 21:40:23,pub,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-177,SOAPxx,,rep-eng,https://github.com/capoe/soapxx,A SOAP implementation.,6,False,['lang-cpp'],GPL-2.0,capoe/soapxx,https://github.com/capoe/soapxx,2016-03-29 10:00:00,2020-03-27 13:47:44.000000,2020-03-27 13:47:36,289.0,,3.0,3.0,1.0,,2.0,7.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-178,COSMO Toolbox,,math,https://github.com/lab-cosmo/toolbox,Assorted libraries and utilities for atomistic simulation analysis.,6,True,['lang-cpp'],,lab-cosmo/toolbox,https://github.com/lab-cosmo/toolbox,2014-05-20 11:23:13,2023-06-23 15:07:59.000000,2023-06-23 15:07:29,106.0,1.0,5.0,26.0,1.0,,,6.0,,,,9.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-179,PANNA,,mliap,https://gitlab.com/PANNAdevs/panna,A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic..,6,False,['benchmarking'],MIT,,,2018-11-09 10:47:48,2018-11-09 10:47:48.000000,,,,10.0,,,,,6.0,,,2.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,PANNAdevs/panna,https://gitlab.com/PANNAdevs/panna,
-180,COSMO Software Cookbook,,educational,https://github.com/lab-cosmo/software-cookbook,The COSMO cookbook contains recipes for atomic-scale modelling for materials and molecules.,6,False,,BSD-3-Clause,lab-cosmo/software-cookbook,https://github.com/lab-cosmo/software-cookbook,2023-05-23 10:33:47,2023-08-15 06:43:12.000000,2023-07-12 12:10:24,23.0,21.0,1.0,13.0,22.0,3.0,,2.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-181,Computational Autonomy for Materials Discovery (CAMD),,materials-discovery,https://github.com/ulissigroup/CAMD,Agent-based sequential learning software for materials discovery.,6,False,,Apache-2.0,ulissigroup/CAMD,https://github.com/ulissigroup/CAMD,2023-01-10 19:42:57,2023-01-10 19:49:35.000000,2023-01-10 19:49:13,1336.0,,,1.0,,,,1.0,,,,17.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,
-182,MEGAN: Multi Explanation Graph Attention Student,,xai,https://github.com/aimat-lab/graph_attention_student,Minimal implementation of graph attention student model architecture.,6,False,,MIT,aimat-lab/graph_attention_student,https://github.com/aimat-lab/graph_attention_student,2022-07-28 06:22:50,2023-08-08 14:03:12.000000,2023-08-08 14:03:07,21.0,9.0,1.0,3.0,1.0,,,1.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-183,GEOM,,datasets,https://github.com/learningmatter-mit/geom,GEOM: Energy-annotated molecular conformations.,5,False,['drug-discovery'],,learningmatter-mit/geom,https://github.com/learningmatter-mit/geom,2020-06-03 17:58:37,2022-04-24 18:57:39.000000,2022-04-24 18:57:39,95.0,,17.0,9.0,,1.0,9.0,131.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-184,AI4Science101,,educational,https://github.com/deepmodeling/AI4Science101,AI for Science.,5,True,,,deepmodeling/AI4Science101,https://github.com/deepmodeling/AI4Science101,2022-06-19 02:26:48,2022-09-04 02:06:18.000000,2022-09-04 02:06:18,139.0,,11.0,10.0,28.0,,1.0,69.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-185,SchNOrb,,ml-wft,https://github.com/atomistic-machine-learning/SchNOrb,Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions.,5,False,,MIT,atomistic-machine-learning/SchNOrb,https://github.com/atomistic-machine-learning/SchNOrb,2019-09-17 12:41:48,2019-09-17 14:31:47.000000,2019-09-17 14:31:19,2.0,,17.0,5.0,,1.0,,53.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-186,Machine Learning for Materials Hard and Soft,,educational,https://github.com/CompPhysVienna/MLSummerSchoolVienna2022,ESI-DCAFM-TACO-VDSP Summer School on Machine Learning for Materials Hard and Soft.,5,False,,,CompPhysVienna/MLSummerSchoolVienna2022,https://github.com/CompPhysVienna/MLSummerSchoolVienna2022,2022-07-01 08:42:41,2022-07-22 08:10:24.000000,2022-07-22 08:10:24,49.0,,16.0,1.0,14.0,,,31.0,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-187,DeepH-E3,,ml-dft,https://github.com/Xiaoxun-Gong/DeepH-E3,General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian.,5,True,['magnetism'],MIT,Xiaoxun-Gong/DeepH-E3,https://github.com/Xiaoxun-Gong/DeepH-E3,2023-03-16 11:25:58,2023-04-04 13:27:01.000000,2023-04-04 13:26:27,16.0,,8.0,4.0,,2.0,5.0,29.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-188,Autobahn,,rep-learn,https://github.com/risilab/Autobahn,Repository for Autobahn: Automorphism Based Graph Neural Networks.,5,False,,MIT,risilab/Autobahn,https://github.com/risilab/Autobahn,2021-03-02 01:14:40,2022-03-01 21:04:09.000000,2022-03-01 21:04:04,11.0,,2.0,5.0,,,,26.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-189,charge_transfer_nnp,,rep-learn,https://github.com/pfnet-research/charge_transfer_nnp,Graph neural network potential with charge transfer.,5,False,['electrostatics'],MIT,pfnet-research/charge_transfer_nnp,https://github.com/pfnet-research/charge_transfer_nnp,2022-04-06 01:48:18,2022-04-06 01:53:35.000000,2022-04-06 01:53:22,1.0,,4.0,12.0,,,,20.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-190,SCFNN,,rep-learn,https://github.com/andy90/SCFNN,Self-consistent determination of long-range electrostatics in neural network potentials.,5,False,"['lang-cpp', 'electrostatics', 'single-paper']",MIT,andy90/SCFNN,https://github.com/andy90/SCFNN,2021-09-22 12:02:00,2022-01-30 02:29:03.000000,2022-01-24 09:40:40,10.0,,8.0,2.0,,,,14.0,2022-01-30 02:29:04,1.0.0,1.0,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-191,rxngenerator,,generative,https://github.com/tsudalab/rxngenerator,A generative model for molecular generation via multi-step chemical reactions.,5,True,,MIT,tsudalab/rxngenerator,https://github.com/tsudalab/rxngenerator,2021-06-18 07:44:53,2022-08-09 07:21:44.000000,2022-08-09 07:21:05,16.0,,2.0,9.0,2.0,1.0,,11.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-192,AGOX,,materials-discovery,https://gitlab.com/agox/agox,AGOX is a package for global optimization of atomic system using e.g. the energy calculated from density functional..,5,False,['structure-optimization'],GPL-3.0,,,2022-03-08 09:08:13,2022-03-08 09:08:13.000000,,,,3.0,,,8.0,5.0,10.0,,,2.0,,agox,,,,https://pypi.org/project/agox,15.0,15.0,,,,3.0,,,,,,,,,,,,,agox/agox,https://gitlab.com/agox/agox,
-193,FieldSchNet,,rep-learn,https://github.com/atomistic-machine-learning/field_schnet,,5,False,,MIT,atomistic-machine-learning/field_schnet,https://github.com/atomistic-machine-learning/field_schnet,2020-11-18 10:26:59,2022-05-19 09:28:38.000000,2022-05-19 09:28:38,26.0,,3.0,3.0,1.0,1.0,,9.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-194,3DSC Database,,datasets,https://github.com/aimat-lab/3DSC,Repo for the paper publishing the superconductor database with 3D crystal structures.,5,True,"['superconductors', 'materials-discovery']",https://github.com/aimat-lab/3DSC/blob/main/LICENSE.md,aimat-lab/3DSC,https://github.com/aimat-lab/3DSC,2021-11-02 09:07:57,2023-07-21 09:28:43.000000,2023-07-21 09:26:12,52.0,6.0,2.0,2.0,,,,6.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-195,Alchemical learning,,mliap,https://github.com/Luthaf/alchemical-learning,Code for the Modeling high-entropy transition metal alloys with alchemical compression article.,5,False,,BSD-3-Clause,Luthaf/alchemical-learning,https://github.com/Luthaf/alchemical-learning,2021-12-02 17:02:00,2023-04-24 18:35:45.000000,2023-04-07 10:19:10,120.0,,1.0,6.0,1.0,,4.0,2.0,,,,10.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-196,BERT-PSIE-TC,,lm,https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC,A dataset of Curie temperatures automatically extracted from scientific literature with the use of the BERT-PSIE..,5,False,['magnetism'],MIT,StefanoSanvitoGroup/BERT-PSIE-TC,https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC,2023-01-25 10:27:26,2023-08-18 11:47:45.000000,2023-08-18 12:48:31,36.0,13.0,2.0,1.0,,,,2.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-197,linear-regression-benchmarks,,datasets,https://github.com/BingqingCheng/linear-regression-benchmarks,Data sets used for linear regression benchmarks.,5,False,"['benchmarking', 'single-paper']",MIT,BingqingCheng/linear-regression-benchmarks,https://github.com/BingqingCheng/linear-regression-benchmarks,2020-04-16 20:48:28,2022-01-26 08:29:46.000000,2022-01-26 08:29:46,24.0,,,3.0,2.0,,,1.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-198,COSMO tools,,others,https://github.com/lab-cosmo/cosmo-tools,"Scripts, jupyter nbs, and general helpful stuff from COSMO by COSMO.",5,False,,,lab-cosmo/cosmo-tools,https://github.com/lab-cosmo/cosmo-tools,2018-11-06 09:40:00,2022-11-25 11:19:48.000000,2022-11-25 11:19:29,59.0,,3.0,22.0,,,,1.0,,,,4.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,True
-199,Per-Site CGCNN,,rep-learn,https://github.com/learningmatter-mit/per-site_cgcnn,Crystal graph convolutional neural networks for predicting material properties.,5,False,"['pre-trained', 'single-paper']",MIT,learningmatter-mit/per-site_cgcnn,https://github.com/learningmatter-mit/per-site_cgcnn,2023-05-30 18:59:03,2023-06-05 17:38:46.000000,2023-06-05 17:38:41,28.0,3.0,,,,,,1.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-200,Visual Graph Datasets,,datasets,https://github.com/aimat-lab/visual_graph_datasets,Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations..,5,True,,MIT,aimat-lab/visual_graph_datasets,https://github.com/aimat-lab/visual_graph_datasets,2023-06-01 11:33:18,2023-06-12 15:19:12.000000,2023-06-12 15:19:08,4.0,4.0,1.0,3.0,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-201,Per-site PAiNN,,rep-learn,https://github.com/learningmatter-mit/per-site_painn,Fork of PaiNN for PerovskiteOrderingGCNNs.,5,True,"['probabilistic', 'pre-trained', 'single-paper']",MIT,learningmatter-mit/per-site_painn,https://github.com/learningmatter-mit/per-site_painn,2023-06-04 14:23:49,2023-06-05 17:35:19.000000,2023-06-05 17:30:34,123.0,1.0,,,,,,,,,,10.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-202,ML-in-chemistry-101,,educational,https://github.com/BingqingCheng/ML-in-chemistry-101,The course materials for Machine Learning in Chemistry 101.,4,False,,,BingqingCheng/ML-in-chemistry-101,https://github.com/BingqingCheng/ML-in-chemistry-101,2020-02-09 17:47:07,2020-10-19 08:10:31.000000,2020-10-19 08:10:30,13.0,,14.0,2.0,,,,54.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-203,Graph Transport Network,,rep-learn,https://github.com/gasteigerjo/gtn,"Graph transport network (GTN), as proposed in Scalable Optimal Transport in High Dimensions for Graph Distances,..",4,False,['transport-phenomena'],https://github.com/gasteigerjo/gtn/blob/main/LICENSE.md,gasteigerjo/gtn,https://github.com/gasteigerjo/gtn,2021-07-11 23:36:22,2023-04-26 14:22:00.000000,2023-04-26 14:22:00,9.0,,3.0,2.0,,,,14.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-204,ACEHAL,,active-learning,https://github.com/ACEsuit/ACEHAL,Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials.,4,False,['lang-julia'],,ACEsuit/ACEHAL,https://github.com/ACEsuit/ACEHAL,2023-02-24 17:33:47,2023-05-09 09:37:27.000000,2023-05-09 09:37:26,107.0,,2.0,6.0,13.0,3.0,5.0,7.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-205,chemrev-gpr,,educational,https://github.com/gabor1/chemrev-gpr,Notebooks accompanying the paper on GPR in materials and molecules in Chemical Reviews 2020.,4,False,,,gabor1/chemrev-gpr,https://github.com/gabor1/chemrev-gpr,2020-12-18 23:48:06,2021-05-04 19:21:34.000000,2021-05-04 19:21:30,10.0,,4.0,4.0,,,,5.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-206,TensorPotential,,mliap,https://cortner.github.io/ACEweb/software/,"Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic..",4,False,,https://github.com/ICAMS/TensorPotential/blob/main/LICENSE.md,ICAMS/TensorPotential,https://github.com/ICAMS/TensorPotential,2021-12-08 12:10:04,2023-07-10 16:37:18.000000,2023-07-10 16:37:18,18.0,1.0,4.0,2.0,2.0,,,5.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-207,MolSLEPA,,generative,https://github.com/tsudalab/MolSLEPA,Interpretable Fragment-based Molecule Design with Self-learning Entropic Population Annealing.,4,False,['xai'],MIT,tsudalab/MolSLEPA,https://github.com/tsudalab/MolSLEPA,2023-04-10 15:04:55,2023-04-13 12:48:49.000000,2023-04-13 12:48:49,11.0,,,8.0,2.0,,,3.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-208,ACEatoms,,general-tool,https://github.com/ACEsuit/ACEatoms.jl,Generic code for modelling atomic properties using ACE.,4,False,['lang-julia'],https://github.com/ACEsuit/ACEatoms.jl/blob/main/ASL.md,ACEsuit/ACEatoms.jl,https://github.com/ACEsuit/ACEatoms.jl,2021-03-23 23:50:03,2023-01-13 21:35:06.000000,2023-01-13 21:28:08,134.0,,1.0,3.0,14.0,4.0,3.0,2.0,,,,10.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-209,charge-density-models,,ml-dft,https://github.com/ulissigroup/charge-density-models,Tools to build charge density models using ocpmodels.,4,False,,MIT,ulissigroup/charge-density-models,https://github.com/ulissigroup/charge-density-models,2022-06-22 13:47:53,2023-07-28 17:17:13.000000,2023-05-18 18:37:07,94.0,,1.0,2.0,14.0,,1.0,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-210,magnetism-prediction,,rep-eng,https://github.com/dppant/magnetism-prediction,DFT-aided Machine Learning Search for Magnetism in Fe-based Bimetallic Chalcogenides.,4,False,"['magnetism', 'single-paper']",Apache-2.0,dppant/magnetism-prediction,https://github.com/dppant/magnetism-prediction,2022-09-13 03:58:10,2023-07-19 13:25:49.000000,2023-07-19 13:25:49,46.0,2.0,,3.0,,,,1.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-211,cnine,,math,https://github.com/risi-kondor/cnine,Cnine tensor library.,4,False,['lang-cpp'],,risi-kondor/cnine,https://github.com/risi-kondor/cnine,2022-10-07 20:54:54,2023-08-24 05:06:32.000000,2023-08-24 05:06:32,187.0,33.0,1.0,1.0,1.0,,,1.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,https://risi-kondor.github.io/cnine/,,,
-212,Wigner Kernels,,math,https://github.com/lab-cosmo/wigner_kernels,Collection of programs to benchmark Wigner kernels.,4,False,['benchmarking'],,lab-cosmo/wigner_kernels,https://github.com/lab-cosmo/wigner_kernels,2022-12-08 12:28:26,2023-07-08 15:48:41.000000,2023-07-08 15:48:37,109.0,4.0,,1.0,,,,,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-213,gprep,,ml-dft,https://gitlab.com/jmargraf/gprep,Fitting DFTB repulsive potentials with GPR.,4,False,['single-paper'],MIT,,,2019-09-30 09:15:04,2019-09-30 09:15:04.000000,,,,0.0,,,,,,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,jmargraf/gprep,https://gitlab.com/jmargraf/gprep,
-214,CSNN,,ml-dft,https://github.com/foxjas/CSNN,Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning.,4,False,,BSD-3-Clause,foxjas/CSNN,https://github.com/foxjas/CSNN,2022-05-19 15:40:49,2022-10-11 04:27:40.000000,2022-10-11 04:27:40,6.0,,,1.0,,,,,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-215,Coarse-Graining-Auto-encoders,,unsupervised,https://github.com/learningmatter-mit/Coarse-Graining-Auto-encoders,,3,False,['single-paper'],,learningmatter-mit/Coarse-Graining-Auto-encoders,https://github.com/learningmatter-mit/Coarse-Graining-Auto-encoders,2019-09-16 15:27:57,2019-08-16 21:39:34.000000,2019-08-16 21:39:33,14.0,,7.0,6.0,,,,19.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-216,ML-DFT,,ml-dft,https://github.com/MihailBogojeski/ml-dft,A package for density functional approximation using machine learning.,3,False,,MIT,MihailBogojeski/ml-dft,https://github.com/MihailBogojeski/ml-dft,2020-09-14 22:15:56,2020-09-18 16:36:30.000000,2020-09-18 16:36:30,9.0,,6.0,2.0,,1.0,1.0,18.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-217,CSPML (crystal structure prediction with machine learning-based element substitution),,materials-discovery,https://github.com/Minoru938/CSPML,Original implementation of CSPML.,3,False,['structure-prediction'],,minoru938/cspml,https://github.com/Minoru938/CSPML,2022-01-15 10:59:27,2022-06-02 23:26:26.000000,2022-06-02 23:26:26,7.0,,7.0,2.0,,1.0,,12.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-218,glp,,mliap,https://github.com/sirmarcel/glp,tools for graph-based machine-learning potentials in jax.,3,False,,MIT,sirmarcel/glp,https://github.com/sirmarcel/glp,2023-03-27 15:19:40,2023-08-07 14:41:10.000000,2023-08-07 14:41:05,10.0,2.0,,1.0,2.0,,,10.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-219,SPINNER,,materials-discovery,https://github.com/MDIL-SNU/SPINNER,SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random..,3,False,"['lang-cpp', 'structure-prediction']",GPL-3.0,MDIL-SNU/SPINNER,https://github.com/MDIL-SNU/SPINNER,2021-07-15 02:10:58,2021-11-25 07:58:15.000000,2021-11-25 07:58:15,102.0,,2.0,1.0,,1.0,,9.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-220,e3psi,,ml-esm,https://github.com/muhrin/e3psi,Equivariant machine learning library for learning from electronic structures.,3,False,,LGPL-3.0,muhrin/e3psi,https://github.com/muhrin/e3psi,2022-08-08 10:48:30,2023-08-09 17:04:49.000000,2023-04-10 17:04:33,14.0,,,2.0,,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-221,atom_by_atom,,rep-learn,https://github.com/learningmatter-mit/atom_by_atom,Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with Machine Learning.,3,False,"['surface-science', 'single-paper']",,learningmatter-mit/atom_by_atom,https://github.com/learningmatter-mit/atom_by_atom,2023-05-30 20:18:00,2023-06-28 20:39:26.000000,2023-06-28 20:39:13,64.0,64.0,,2.0,,,,2.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-222,SISSO++,,rep-eng,https://gitlab.com/sissopp_developers/sissopp,C++ Implementation of SISSO with python bindings.,3,False,['lang-cpp'],Apache-2.0,,,2021-04-30 14:20:59,2021-04-30 14:20:59.000000,,,,2.0,,,1.0,11.0,1.0,,,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,sissopp_developers/sissopp,https://gitlab.com/sissopp_developers/sissopp,
-223,Magpie,,general-tool,https://bitbucket.org/wolverton/magpie/,Materials Agnostic Platform for Informatics and Exploration (Magpie).,3,False,['lang-java'],MIT,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-224,MALADA,,ml-dft,https://github.com/mala-project/malada,MALA Data Acquisition: Helpful tools to build data for MALA.,3,False,,BSD-3-Clause,mala-project/malada,https://github.com/mala-project/malada,2021-07-26 05:46:08,2023-05-24 09:18:24.000000,2023-05-24 09:18:24,111.0,,1.0,2.0,4.0,17.0,2.0,,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-225,MLatom,,general-tool,http://mlatom.com/,Machine learning for atomistic simulations.,3,False,,https://creativecommons.org/licenses/by-nc-nd/4.0/,,,,,,,,,,,,,,,,,,MLatom,,,,https://pypi.org/project/MLatom,105.0,105.0,,,,3.0,,,,,,,,,,,,http://mlatom.com/manual/,,,
-226,xDeepH,,ml-dft,https://github.com/mzjb/xDeepH,Extended DeepH (xDeepH) method for magnetic materials.,2,False,"['magnetism', 'lang-julia']",LGPL-3.0,mzjb/xDeepH,https://github.com/mzjb/xDeepH,2023-02-23 12:56:49,2023-06-14 11:44:53.000000,2023-06-14 11:44:46,4.0,1.0,,1.0,,,,16.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-227,MLIP-3,,mliap,https://gitlab.com/ashapeev/mlip-3,MLIP-3: Active learning on atomic environments with Moment Tensor Potentials (MTP).,2,False,['lang-cpp'],BSD-2-Clause,,,2023-04-24 14:05:53,2023-04-24 14:05:53.000000,,,,1.0,,,10.0,1.0,11.0,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,ashapeev/mlip-3,https://gitlab.com/ashapeev/mlip-3,
-228,SingleNN,,mliap,https://github.com/lmj1029123/SingleNN,An efficient package for training and executing neural-network interatomic potentials.,2,False,['lang-cpp'],,lmj1029123/SingleNN,https://github.com/lmj1029123/SingleNN,2020-03-11 18:36:16,2021-11-09 00:40:18.000000,2021-11-09 00:40:10,17.0,,1.0,1.0,,1.0,,6.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-229,interface-lammps-mlip-3,,md,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,An interface between LAMMPS and MLIP (version 3).,2,False,,GPL-2.0,,,2023-04-24 12:48:51,2023-04-24 12:48:51.000000,,,,2.0,,,,,5.0,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,ivannovikov/interface-lammps-mlip-3,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,
-230,MLDensity_tutorial,,educational,https://github.com/bfocassio/MLDensity_tutorial,Tutorial files to work with ML for the charge density in molecules and solids.,2,False,,,bfocassio/MLDensity_tutorial,https://github.com/bfocassio/MLDensity_tutorial,2023-01-31 10:33:23,2023-02-22 19:20:32.000000,2023-02-22 19:20:32,8.0,,1.0,1.0,,,,4.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-231,quantum-structure-ml,,general-tool,https://github.com/hgheiberger/quantum-structure-ml,Multi-class classification model for predicting the magnetic order of magnetic structures and a binary classification..,2,False,"['magnetism', 'benchmarking']",,hgheiberger/quantum-structure-ml,https://github.com/hgheiberger/quantum-structure-ml,2020-10-05 01:11:01,2022-12-22 21:45:40.000000,2022-12-22 21:45:40,19.0,,,2.0,,,,1.0,2022-08-18 05:25:24,1.0.0,1.0,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-232,ACE Workflows,,mliap,https://github.com/ACEsuit/ACEworkflows,Workflow Examples for ACE Models.,2,False,"['lang-julia', 'workflows']",,ACEsuit/ACEworkflows,https://github.com/ACEsuit/ACEworkflows,2023-04-04 16:57:36,2023-08-21 23:24:43.000000,2023-08-21 23:24:40,19.0,4.0,,3.0,6.0,1.0,,,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-233,AMP,,rep-eng,https://bitbucket.org/andrewpeterson/amp/,Amp is an open-source package designed to easily bring machine-learning to atomistic calculations.,2,False,,,,,,,,,,,,,,,,,,,,amp-atomistics,,,,https://pypi.org/project/amp-atomistics,43.0,43.0,,,,3.0,,,,,,,,,,,,https://amp.readthedocs.io/,,,
-234,q-pac,,ml-esm,,,2,False,['electrostatics'],MIT,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,jmargraf/kqeq,,
-235,RuNNer,,mliap,https://www.uni-goettingen.de/de/560580.html,The RuNNer Neural Network Energy Representation is a Fortran-based framework for the construction of Behler-..,2,False,['lang-fortran'],GPL-3.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,https://theochemgoettingen.gitlab.io/RuNNer/,,,
-236,Point Edge Transformer,,rep-learn,https://zenodo.org/record/7967079,"Smooth, exact rotational symmetrization for deep learning on point clouds.",2,False,,CC-BY-4.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-237,PiNN Lab,,educational,https://github.com/Teoroo-CMC/PiNN_lab,,1,False,,GPL-3.0,Teoroo-CMC/PiNN_lab,https://github.com/Teoroo-CMC/PiNN_lab,2019-03-17 22:09:30,2023-05-01 15:59:56.000000,2023-05-01 15:59:22,9.0,,1.0,2.0,1.0,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-238,gkx: Green-Kubo Method in JAX,,rep-learn,https://github.com/sirmarcel/gkx,Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast.,1,False,['transport-phenomena'],MIT,sirmarcel/gkx,https://github.com/sirmarcel/gkx,2023-04-30 12:25:16,2023-04-30 14:14:57.000000,2023-04-30 14:14:46,2.0,,,1.0,,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-239,kdft,,ml-dft,https://gitlab.com/jmargraf/kdf,The Kernel Density Functional (KDF) code allows generating ML based DFT functionals.,1,False,,,,,2020-11-07 21:50:22,2020-11-07 21:50:22.000000,,,,0.0,,,,,2.0,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,jmargraf/kdf,https://gitlab.com/jmargraf/kdf,
-240,MALA Tutorial,,educational,https://github.com/mala-project/mala_tutorial,A full MALA hands-on tutorial.,1,False,,,mala-project/mala_tutorial,https://github.com/mala-project/mala_tutorial,2023-03-09 14:01:54,2023-03-20 12:13:27.000000,2023-03-20 12:13:17,23.0,,,2.0,,,,1.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,
-241,Descriptor Embedding and Clustering for Atomisitic-environment Framework (DECAF),,unsupervised,https://gitlab.mpcdf.mpg.de/klai/decaf,Provides a workflow to obtain clustering of local environments in dataset of structures.,0,False,,,,,,,,41.0,,,,,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,
-242,SphericalNet,,rep-learn,https://github.com/risilab/SphericalNet,Implementation of Clebsch-Gordan Networks (CGnet: https://arxiv.org/pdf/1806.09231.pdf) by GElib & cnine libraries in..,0,False,,,risilab/SphericalNet,https://github.com/risilab/SphericalNet,2022-05-31 14:39:05,2022-06-07 03:57:10.000000,2022-06-07 03:53:49,1.0,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-243,KmdPlus,,unsupervised,https://github.com/Minoru938/KmdPlus,"This module contains a class for treating kernel mean descriptor (KMD), and a function for generating descriptors with..",0,False,,,Minoru938/KmdPlus,https://github.com/Minoru938/KmdPlus,2023-03-26 10:06:34,2023-03-26 11:03:39.000000,2023-03-26 11:03:39,4.0,,,1.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-244,GitHub topic materials-informatics,,community,https://github.com/topics/materials-informatics,,0,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,,,,,,,,
-245,MLDensity,,ml-dft,{},Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure..,0,False,,,StefanoSanvitoGroup/MLdensity,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,name,resource,category,license,homepage,description,projectrank,show,labels,github_id,github_url,created_at,updated_at,last_commit_pushed_at,commit_count,recent_commit_count,fork_count,watchers_count,pr_count,open_issue_count,closed_issue_count,star_count,latest_stable_release_published_at,latest_stable_release_number,release_count,contributor_count,pypi_id,conda_id,dependent_project_count,github_dependent_project_count,pypi_url,pypi_monthly_downloads,monthly_downloads,conda_url,conda_latest_release_published_at,conda_total_downloads,projectrank_placing,dockerhub_id,dockerhub_url,dockerhub_latest_release_published_at,dockerhub_stars,dockerhub_pulls,github_release_downloads,maven_id,maven_url,new_addition,npm_id,npm_url,npm_monthly_downloads,docs_url,gitlab_id,gitlab_url,ignore
+0,AI for Science Map,True,community,GPL-3.0 license,https://www.air4.science/map,"Interactive mindmap of the AI4Science research field, including atomistic machine learning, including papers,..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,Atomic Cluster Expansion,True,community,,https://cortner.github.io/ACEweb/,Atomic Cluster Expansion (ACE) community homepage.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,CrystaLLM,True,community,https://materialis.ai/terms.html,https://crystallm.com,,0,True,"['lm', 'generative', 'pre-trained']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,matsci.org,True,community,,https://matsci.org/,"A community forum for the discussion of anything materials science, with a focus on computational materials science..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4,Matter Modeling Stack Exchange - Machine Learning,True,community,,https://mattermodeling.stackexchange.com/questions/tagged/machine-learning,"Forum StackExchange, site Matter Modeling, ML-tagged questions.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+5,2DMD dataset,True,datasets,,https://rolos.com/open/2d-materials-point-defects/#physics,,0,True,['material-defect'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,Catalysis Hub,True,datasets,,https://www.catalysis-hub.org/,A web-platform for sharing data and software for computational catalysis research!.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+7,Citrination Datasets,True,datasets,MIT,https://citrination.com/datasets,,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,crystals.ai,True,datasets,,https://crystals.ai/,Curated datasets for reproducible AI in materials science.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,DeepChem Models,True,datasets,,https://huggingface.co/DeepChem,,0,True,"['pre-trained', 'lm']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+10,JARVIS-Leaderboard,True,datasets,https://github.com/usnistgov/jarvis_leaderboard/blob/main/LICENSE.rst,https://pages.nist.gov/jarvis_leaderboard/,This project provides benchmark-performances for materials science applications including Artificial Intelligence..,0,True,['benchmarking'],usnistgov/jarvis_leaderboard,https://github.com/usnistgov/jarvis_leaderboard,2022-07-15 16:48:33,2023-08-16 14:30:48.000000,2023-08-16 14:26:39,673.0,202.0,24.0,5.0,228.0,4.0,2.0,39.0,2023-08-04 17:33:22,2023.08.01,21.0,24.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,Materials Project - Charge Densities,True,datasets,,https://materialsproject.org/ml/charge_densities,,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+12,matterverse.ai,True,datasets,,https://matterverse.ai/,Database of yet-to-be-sythesized materials predicted using state-of-the-art machine learning algorithms.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,Quantum-Machine.org Datasets,True,datasets,,http://quantum-machine.org/datasets/,"Collection of datasets, including QM7, QM9, etc. MD, DFT. Small organic molecules, mostly.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+14,sGDML Datasets,True,datasets,,http://sgdml.org/#datasets,,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,Quantum Chemistry in the Age of Machine Learning,True,educational,,https://www.elsevier.com/books-and-journals/book-companion/9780323900492,,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+16,Pre-trained OCP models,True,mliap,,https://github.com/Open-Catalyst-Project/ocp/blob/main/MODELS.md,Pre-trained models released as part of the Open Catalyst Project.,0,True,['pre-trained'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+17,PyG Models,True,rep-learn,,https://github.com/pyg-team/pytorch_geometric/tree/master/torch_geometric/nn/models,Representation learning models implemented in PyTorch Geometric.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+18,Deep Graph Library (DGL),,rep-learn,Apache-2.0,https://github.com/dmlc/dgl,"Python package built to ease deep learning on graph, on top of existing DL frameworks.",38,True,,dmlc/dgl,https://github.com/dmlc/dgl,2018-04-20 14:49:09,2023-08-25 11:11:33.000000,2023-08-25 06:39:54,3342.0,286.0,2790.0,170.0,3879.0,300.0,2027.0,12117.0,2023-08-15 07:31:40,1.1.2,75.0,273.0,dgl,dglteam/dgl,138.0,138.0,https://pypi.org/project/dgl,119087.0,124084.0,https://anaconda.org/dglteam/dgl,2023-08-17 01:25:44.402,279875.0,1.0,,,,,,,,,,,,,,,,
+19,DeepChem,,general-tool,MIT,https://github.com/deepchem/deepchem,"Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology.",36,True,,deepchem/deepchem,https://github.com/deepchem/deepchem,2015-09-24 23:20:28,2023-08-25 09:45:10.000000,2023-08-23 19:15:57,9195.0,300.0,1451.0,148.0,1925.0,411.0,1204.0,4552.0,2022-12-01 13:22:37,2.7.1,18.0,224.0,deepchem,conda-forge/deepchem,244.0,244.0,https://pypi.org/project/deepchem,10705.0,13220.0,https://anaconda.org/conda-forge/deepchem,2023-06-16 19:18:02.015,102574.0,1.0,deepchemio/deepchem,https://hub.docker.com/r/deepchemio/deepchem,2022-03-11 05:24:00.723691,4.0,6948.0,,,,,,,,,,,
+20,RDKit,,general-tool,BSD-3-Clause,https://github.com/rdkit/rdkit,,30,True,['lang-cpp'],rdkit/rdkit,https://github.com/rdkit/rdkit,2013-05-12 06:19:15,2023-08-25 14:13:21.000000,2023-08-25 04:09:19,7456.0,70.0,750.0,85.0,2784.0,838.0,2031.0,2162.0,2023-08-17 05:41:56,Release_2023_03_3,95.0,201.0,rdkit,rdkit/rdkit,2.0,2.0,https://pypi.org/project/rdkit,189831.0,212761.0,https://anaconda.org/rdkit/rdkit,2023-06-16 12:54:07.547,2521147.0,1.0,,,,,,1397.0,,,,,,,,,,
+21,Matminer,,general-tool,https://github.com/hackingmaterials/matminer/blob/main/LICENSE,https://github.com/hackingmaterials/matminer,Data mining for materials science.,29,True,,hackingmaterials/matminer,https://github.com/hackingmaterials/matminer,2015-09-24 20:37:00,2023-08-21 08:07:39.000000,2023-06-27 15:45:29,4108.0,11.0,173.0,28.0,697.0,25.0,187.0,400.0,2023-06-27 15:36:52,0.9.0,66.0,49.0,matminer,conda-forge/matminer,222.0,222.0,https://pypi.org/project/matminer,16620.0,18041.0,https://anaconda.org/conda-forge/matminer,2023-06-27 20:24:06.404,48343.0,1.0,,,,,,,,,,,,,,,,
+22,DeePMD-kit,,mliap,LGPL-3.0,https://github.com/deepmodeling/deepmd-kit,A deep learning package for many-body potential energy representation and molecular dynamics.,28,True,['lang-cpp'],deepmodeling/deepmd-kit,https://github.com/deepmodeling/deepmd-kit,2017-12-12 15:23:44,2023-08-25 03:39:10.000000,2023-08-08 03:52:54,2223.0,78.0,426.0,47.0,1223.0,50.0,390.0,1189.0,2023-08-08 05:00:42,2.2.3,37.0,60.0,deepmd-kit,deepmodeling/deepmd-kit,11.0,11.0,https://pypi.org/project/deepmd-kit,792.0,1384.0,https://anaconda.org/deepmodeling/deepmd-kit,2023-08-11 22:29:42.658,809.0,1.0,deepmodeling/deepmd-kit,https://hub.docker.com/r/deepmodeling/deepmd-kit,2023-08-13 18:58:55.024799,1.0,1981.0,24614.0,,,,,,,,,,
+23,SchNetPack,,rep-learn,MIT,https://github.com/atomistic-machine-learning/schnetpack,SchNetPack - Deep Neural Networks for Atomistic Systems.,27,True,,atomistic-machine-learning/schnetpack,https://github.com/atomistic-machine-learning/schnetpack,2018-09-03 15:44:35,2023-08-18 13:26:08.000000,2023-08-17 11:11:14,1555.0,12.0,177.0,31.0,358.0,3.0,205.0,630.0,2023-04-25 11:23:00,2.0.3,7.0,31.0,schnetpack,,56.0,56.0,https://pypi.org/project/schnetpack,469.0,469.0,,,,1.0,,,,,,,,,,,,,,,,
+24,QUIP,,general-tool,GPL-2.0,https://github.com/libAtoms/QUIP,libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io.,27,True,,libAtoms/QUIP,https://github.com/libAtoms/QUIP,2013-07-02 15:21:59,2023-07-18 16:21:18.000000,2023-07-18 16:06:47,10831.0,44.0,117.0,27.0,163.0,88.0,335.0,297.0,2023-06-15 19:11:24,0.9.14,15.0,79.0,quippy-ase,,22.0,22.0,https://pypi.org/project/quippy-ase,1439.0,1527.0,,,,2.0,libatomsquip/quip,https://hub.docker.com/r/libatomsquip/quip,2023-04-24 21:25:17.345957,4.0,9847.0,346.0,,,,,,,,,,
+25,paper-qa,,lm,Apache-2.0,https://github.com/whitead/paper-qa,LLM Chain for answering questions from documents with citations.,26,True,,whitead/paper-qa,https://github.com/whitead/paper-qa,2023-02-05 01:07:25,2023-08-24 05:38:45.000000,2023-08-24 04:38:32,165.0,44.0,270.0,38.0,75.0,39.0,59.0,2962.0,2023-08-24 05:38:45,3.8.0,63.0,12.0,paper-qa,,25.0,25.0,https://pypi.org/project/paper-qa,3711.0,3711.0,,,,1.0,,,,,,,,,,,,,,,,
+26,cdk,,rep-eng,LGPL-2.1,https://github.com/cdk/cdk,The Chemistry Development Kit.,25,True,"['cheminformatics', 'lang-java']",cdk/cdk,https://github.com/cdk/cdk,2010-05-11 08:30:07,2023-08-25 12:44:48.000000,2023-08-24 15:22:54,17406.0,81.0,145.0,40.0,748.0,21.0,231.0,427.0,2023-08-21 19:50:47,cdk-2.9,20.0,163.0,,,18.0,18.0,,,137.0,,,,1.0,,,,,,14715.0,org.openscience.cdk:cdk-bundle,https://search.maven.org/artifact/org.openscience.cdk/cdk-bundle,True,,,,,,,
+27,e3nn,,rep-learn,MIT,https://github.com/e3nn/e3nn,A modular framework for neural networks with Euclidean symmetry.,24,True,,e3nn/e3nn,https://github.com/e3nn/e3nn,2020-01-31 13:06:42,2023-08-15 13:48:27.000000,2023-08-15 13:48:22,2156.0,4.0,106.0,19.0,202.0,17.0,129.0,726.0,2022-12-12 21:42:03,0.5.1,28.0,28.0,e3nn,conda-forge/e3nn,102.0,102.0,https://pypi.org/project/e3nn,13184.0,13756.0,https://anaconda.org/conda-forge/e3nn,2023-06-18 08:41:30.723,8594.0,1.0,,,,,,,,,,,,,,,,
+28,AlphaFold,,biomolecules,Apache-2.0,https://github.com/deepmind/alphafold,Open source code for AlphaFold.,23,True,,deepmind/alphafold,https://github.com/deepmind/alphafold,2021-06-17 14:06:06,2023-08-10 17:19:05.000000,2023-08-10 13:28:00,125.0,5.0,1837.0,204.0,81.0,166.0,565.0,10658.0,2023-04-05 09:45:53,2.3.2,13.0,19.0,,,6.0,6.0,,,,,,,1.0,,,,,,,,,,,,,,,,
+29,JAX-MD,,md,Apache-2.0,https://github.com/jax-md/jax-md,"Differentiable, Hardware Accelerated, Molecular Dynamics.",23,True,,jax-md/jax-md,https://github.com/jax-md/jax-md,2019-05-13 21:03:37,2023-08-22 22:45:59.000000,2023-08-22 22:45:58,836.0,42.0,159.0,50.0,153.0,60.0,73.0,980.0,2022-11-27 12:42:00,jax-md-v0.2.24,7.0,28.0,jax-md,,33.0,33.0,https://pypi.org/project/jax-md,1891.0,1891.0,,,,1.0,,,,,,,,,,,,,,,,
+30,dgl-lifesci,,rep-learn,Apache-2.0,https://github.com/awslabs/dgl-lifesci,Python package for graph neural networks in chemistry and biology.,23,True,,awslabs/dgl-lifesci,https://github.com/awslabs/dgl-lifesci,2020-04-23 07:14:21,2023-06-12 18:56:16.000000,2023-04-16 03:55:52,236.0,,133.0,17.0,139.0,24.0,56.0,606.0,2023-02-13 08:45:17,0.3.2,8.0,22.0,dgllife,,108.0,108.0,https://pypi.org/project/dgllife,12889.0,12889.0,,,,1.0,,,,,,,,,,,,,,,,
+31,JARVIS-Tools,,general-tool,https://github.com/usnistgov/jarvis/blob/master/LICENSE.rst,https://github.com/usnistgov/jarvis,JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications:..,23,True,,usnistgov/jarvis,https://github.com/usnistgov/jarvis,2017-06-22 19:34:02,2023-08-20 02:08:18.000000,2023-08-20 02:07:14,2093.0,6.0,105.0,27.0,221.0,38.0,43.0,239.0,2023-08-11 17:26:26,2023.08.01,69.0,15.0,jarvis-tools,conda-forge/jarvis-tools,60.0,60.0,https://pypi.org/project/jarvis-tools,15833.0,17448.0,https://anaconda.org/conda-forge/jarvis-tools,2023-06-16 19:23:23.093,54938.0,2.0,,,,,,,,,,,,,,,,
+32,MPContribs,,datasets,MIT,https://github.com/materialsproject/MPContribs,Platform for materials scientists to contribute and disseminate their materials data through Materials Project.,23,True,,materialsproject/MPContribs,https://github.com/materialsproject/MPContribs,2014-12-11 18:25:27,2023-08-21 17:13:29.000000,2023-08-21 17:13:28,5266.0,78.0,20.0,11.0,1515.0,20.0,78.0,32.0,2023-08-04 19:37:39,5.4.3,61.0,25.0,mpcontribs-client,,21.0,21.0,https://pypi.org/project/mpcontribs-client,3126.0,3126.0,,,,1.0,,,,,,,,,True,,,,,,,
+33,Best-of Machine Learning with Python,,community,CC-BY-4.0,https://github.com/ml-tooling/best-of-ml-python,A ranked list of awesome machine learning Python libraries. Updated weekly.,22,True,"['general-ml', 'lang-py']",ml-tooling/best-of-ml-python,https://github.com/ml-tooling/best-of-ml-python,2020-11-29 19:41:36,2023-08-24 15:01:00.000000,2023-08-24 15:00:59,412.0,29.0,2040.0,376.0,216.0,15.0,32.0,14233.0,2023-08-24 15:01:06,2023.08.24,100.0,41.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
+34,NVIDIA Deep Learning Examples for Tensor Cores,,rep-learn,https://github.com/NVIDIA/DeepLearningExamples/blob/master/DGLPyTorch/DrugDiscovery/SE3Transformer/LICENSE,https://github.com/NVIDIA/DeepLearningExamples#graph-neural-networks,State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and..,22,True,"['educational', 'drug-discovery']",NVIDIA/DeepLearningExamples,https://github.com/NVIDIA/DeepLearningExamples,2018-05-02 17:04:05,2023-08-23 10:13:19.000000,2023-08-23 10:09:12,1424.0,12.0,2755.0,289.0,523.0,217.0,556.0,11389.0,,,,115.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
+35,TorchANI,,mliap,MIT,https://github.com/aiqm/torchani,Accurate Neural Network Potential on PyTorch.,22,True,,aiqm/torchani,https://github.com/aiqm/torchani,2018-04-02 15:43:04,2023-06-25 04:24:10.593000,2023-03-21 14:30:59,433.0,,109.0,27.0,478.0,17.0,137.0,392.0,2023-04-13 18:10:53,2.2.3,23.0,16.0,torchani,conda-forge/torchani,25.0,25.0,https://pypi.org/project/torchani,1752.0,7583.0,https://anaconda.org/conda-forge/torchani,2023-06-25 04:24:10.593,209947.0,1.0,,,,,,,,,,,,,,,,
+36,DScribe,,rep-eng,Apache-2.0,https://github.com/SINGROUP/dscribe,DScribe is a python package for creating machine learning descriptors for atomistic systems.,22,True,,SINGROUP/dscribe,https://github.com/SINGROUP/dscribe,2017-05-08 08:29:51,2023-08-14 19:17:34.000000,2023-07-19 15:24:32,1284.0,9.0,78.0,21.0,24.0,8.0,77.0,341.0,,,13.0,18.0,dscribe,conda-forge/dscribe,145.0,145.0,https://pypi.org/project/dscribe,5358.0,7424.0,https://anaconda.org/conda-forge/dscribe,2023-07-19 19:51:35.626,76451.0,1.0,,,,,,,,,,,,,,,,
+37,MAML,,general-tool,BSD-3-Clause,https://github.com/materialsvirtuallab/maml,"Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.",22,True,,materialsvirtuallab/maml,https://github.com/materialsvirtuallab/maml,2020-01-25 15:04:21,2023-08-22 13:52:36.000000,2023-08-16 03:01:53,1553.0,130.0,58.0,21.0,497.0,4.0,58.0,261.0,2022-09-20 18:44:37,2022.9.20,12.0,27.0,maml,,4.0,4.0,https://pypi.org/project/maml,128.0,128.0,,,,2.0,,,,,,,,,,,,,,,,
+38,DP-GEN,,mliap,LGPL-3.0,https://github.com/deepmodeling/dpgen,The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field.,22,True,['workflows'],deepmodeling/dpgen,https://github.com/deepmodeling/dpgen,2019-06-13 11:43:56,2023-08-25 06:13:47.000000,2023-03-21 14:37:04,1958.0,,154.0,13.0,718.0,42.0,194.0,229.0,2023-03-22 17:49:22,0.11.1,16.0,59.0,dpgen,deepmodeling/dpgen,4.0,4.0,https://pypi.org/project/dpgen,184.0,221.0,https://anaconda.org/deepmodeling/dpgen,2023-06-16 19:27:03.566,162.0,1.0,,,,,,1430.0,,,,,,,,,,
+39,kgcnn,,rep-learn,MIT,https://github.com/aimat-lab/gcnn_keras,Graph convolution with tf.keras.,22,True,,aimat-lab/gcnn_keras,https://github.com/aimat-lab/gcnn_keras,2020-07-17 11:12:46,2023-08-17 09:42:40.000000,2023-08-17 09:38:11,2758.0,76.0,24.0,7.0,30.0,5.0,72.0,80.0,2023-08-01 15:42:10,3.0.2,23.0,7.0,kgcnn,,15.0,15.0,https://pypi.org/project/kgcnn,408.0,408.0,,,,1.0,,,,,,,,,,,,,,,,
+40,DM21,,ml-dft,Apache-2.0,https://github.com/deepmind/deepmind-research/tree/master/density_functional_approximation_dm21,This package provides a PySCF interface to the DM21 (DeepMind 21) family of exchange-correlation functionals described..,21,True,,deepmind/deepmind-research,https://github.com/deepmind/deepmind-research,2019-01-15 09:54:13,2023-08-17 01:03:23.000000,2023-06-02 17:04:50,369.0,13.0,2397.0,338.0,147.0,154.0,134.0,12053.0,,,,92.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
+41,MEGNet,,mliap,BSD-3-Clause,https://github.com/materialsvirtuallab/megnet,Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals.,21,True,,materialsvirtuallab/megnet,https://github.com/materialsvirtuallab/megnet,2018-12-12 21:31:28,2023-04-27 02:39:17.000000,2023-04-27 02:39:17,1146.0,,140.0,24.0,314.0,17.0,57.0,455.0,2022-11-16 21:24:36,1.3.2,34.0,13.0,megnet,,69.0,69.0,https://pypi.org/project/megnet,692.0,692.0,,,,1.0,,,,,,,,,,,,,,,,
+42,dpdata,,data-structures,LGPL-3.0,https://github.com/deepmodeling/dpdata,"Manipulating multiple atomic simulation data formats, including DeePMD-kit, VASP, LAMMPS, ABACUS, etc.",21,True,,deepmodeling/dpdata,https://github.com/deepmodeling/dpdata,2019-04-12 13:24:23,2023-08-23 02:14:20.000000,2023-05-04 01:19:46,604.0,,101.0,8.0,369.0,22.0,46.0,138.0,2023-05-04 01:23:26,0.2.15,22.0,45.0,dpdata,deepmodeling/dpdata,100.0,100.0,https://pypi.org/project/dpdata,2301.0,2321.0,https://anaconda.org/deepmodeling/dpdata,2023-06-16 19:27:06.316,489.0,1.0,,,,,,,,,,,,,,,,
+43,NequIP,,mliap,MIT,https://github.com/mir-group/nequip,NequIP is a code for building E(3)-equivariant interatomic potentials.,20,True,,mir-group/nequip,https://github.com/mir-group/nequip,2021-03-15 23:44:39,2023-08-17 18:10:06.000000,2023-03-26 21:37:08,1670.0,,90.0,18.0,149.0,12.0,48.0,420.0,2022-12-20 18:52:46,0.5.6,14.0,8.0,nequip,conda-forge/nequip,14.0,14.0,https://pypi.org/project/nequip,505.0,724.0,https://anaconda.org/conda-forge/nequip,2023-06-18 08:41:30.787,3288.0,1.0,,,,,,,,,,,,,,,,
+44,DeepQMC,,ml-wft,MIT,https://github.com/deepqmc/deepqmc,Deep learning quantum Monte Carlo for electrons in real space.,20,True,,deepqmc/deepqmc,https://github.com/deepqmc/deepqmc,2019-12-06 14:50:59,2023-08-25 14:45:11.000000,2023-08-08 09:18:02,1278.0,66.0,56.0,22.0,124.0,6.0,30.0,297.0,2023-01-04 09:38:38,1.0.1,7.0,13.0,deepqmc,,1.0,1.0,https://pypi.org/project/deepqmc,50.0,50.0,,,,1.0,,,,,,,,,,,,,,,,
+45,MatGL (Materials Graph Library),,rep-learn,BSD-3-Clause,https://github.com/materialsvirtuallab/matgl,Graph deep learning library for materials.,20,True,,materialsvirtuallab/matgl,https://github.com/materialsvirtuallab/matgl,2022-08-29 18:36:05,2023-08-24 17:04:44.000000,2023-08-24 17:04:43,831.0,439.0,24.0,7.0,109.0,1.0,33.0,110.0,2023-08-22 14:09:18,0.8.3,21.0,11.0,m3gnet,,2.0,2.0,https://pypi.org/project/m3gnet,804.0,804.0,,,,2.0,,,,,,,,,,,,,,,,
+46,ocp,,rep-learn,MIT,https://github.com/Open-Catalyst-Project/ocp,ocp is the Open Catalyst Projects library of state-of-the-art machine learning algorithms for catalysis.,19,True,,Open-Catalyst-Project/ocp,https://github.com/Open-Catalyst-Project/ocp,2019-09-26 04:47:27,2023-08-24 22:21:36.000000,2023-08-24 22:13:39,697.0,23.0,166.0,24.0,442.0,12.0,113.0,448.0,2022-10-01 03:00:41,0.1.0,4.0,31.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+47,exmol,,xai,MIT,https://github.com/ur-whitelab/exmol,Explainer for black box models that predict molecule properties.,19,True,,ur-whitelab/exmol,https://github.com/ur-whitelab/exmol,2021-08-03 17:56:06,2023-06-19 20:50:50.000000,2023-06-19 19:49:27,188.0,1.0,37.0,9.0,73.0,10.0,57.0,251.0,2023-06-19 20:50:51,3.0.3,27.0,7.0,exmol,,12.0,12.0,https://pypi.org/project/exmol,421.0,421.0,,,,1.0,,,,,,,,,,,,,,,,
+48,FLARE,,active-learning,MIT,https://github.com/mir-group/flare,An open-source Python package for creating fast and accurate interatomic potentials.,19,True,"['lang-cpp', 'mliap']",mir-group/flare,https://github.com/mir-group/flare,2018-08-30 23:40:56,2023-07-20 11:31:36.000000,2023-05-26 02:06:09,4382.0,,55.0,18.0,184.0,19.0,160.0,240.0,2022-04-21 18:33:10,0.2.4,5.0,36.0,,,10.0,10.0,,,0.0,,,,1.0,,,,,,1.0,,,,,,,,,,
+49,FitSNAP,,md,GPL-2.0,https://github.com/FitSNAP/FitSNAP,Software for generating SNAP machine-learning interatomic potentials.,19,True,,FitSNAP/FitSNAP,https://github.com/FitSNAP/FitSNAP,2019-09-12 14:46:18,2023-08-25 00:10:17.000000,2023-08-22 15:25:33,1284.0,55.0,42.0,7.0,164.0,8.0,54.0,121.0,2023-06-28 16:00:48,3.1.0,7.0,24.0,,conda-forge/fitsnap3,,,,,133.0,https://anaconda.org/conda-forge/fitsnap3,2023-06-16 00:19:04.615,4358.0,2.0,,,,,,5.0,,,,,,,,,,
+50,Graph-based Deep Learning Literature,,community,MIT,https://github.com/naganandy/graph-based-deep-learning-literature,links to conference publications in graph-based deep learning.,18,True,"['general-ml', 'rep-learn']",naganandy/graph-based-deep-learning-literature,https://github.com/naganandy/graph-based-deep-learning-literature,2017-12-01 14:48:35,2023-08-24 11:07:22.000000,2023-08-13 09:28:41,7606.0,35.0,700.0,242.0,21.0,,13.0,4294.0,,,,11.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+51,Open Catalyst datasets,,datasets,CC-BY-4.0,https://github.com/Open-Catalyst-Project/ocp/blob/main/DATASET.md,"The datasets of the Open Catalyst project, OC20, OC22.",18,True,,Open-Catalyst-Project/ocp,https://github.com/Open-Catalyst-Project/ocp,2019-09-26 04:47:27,2023-08-24 22:21:36.000000,2023-08-24 22:13:39,697.0,23.0,166.0,24.0,442.0,12.0,113.0,448.0,2022-10-01 03:00:41,0.1.0,4.0,31.0,,,,,,,,,,,1.0,,,,,,,,,True,,,,,,,
+52,GT4SD,,generative,MIT,https://github.com/GT4SD/gt4sd-core,"GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process.",18,True,"['pre-trained', 'drug-discovery', 'rep-learn']",GT4SD/gt4sd-core,https://github.com/GT4SD/gt4sd-core,2022-02-11 19:06:58,2023-08-25 06:45:06.000000,2023-08-25 06:14:51,279.0,4.0,53.0,13.0,135.0,2.0,87.0,234.0,2023-05-06 06:55:52,1.3.1,54.0,19.0,gt4sd,,,,https://pypi.org/project/gt4sd,629.0,629.0,,,,1.0,,,,,,,,,True,,,,,,,
+53,M3GNet,,mliap,BSD-3-Clause,https://github.com/materialsvirtuallab/m3gnet,Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art..,18,True,,materialsvirtuallab/m3gnet,https://github.com/materialsvirtuallab/m3gnet,2022-01-18 18:10:58,2023-06-06 23:56:08.000000,2023-06-06 23:56:03,261.0,4.0,50.0,10.0,33.0,15.0,20.0,173.0,2022-11-17 23:25:35,0.2.4,16.0,14.0,m3gnet,,16.0,16.0,https://pypi.org/project/m3gnet,804.0,804.0,,,,2.0,,,,,,,,,,,,,,,,
+54,ALIGNN,,rep-learn,https://github.com/usnistgov/alignn/blob/main/LICENSE.rst,https://github.com/usnistgov/alignn,Atomistic Line Graph Neural Network.,18,True,,usnistgov/alignn,https://github.com/usnistgov/alignn,2021-04-19 20:08:09,2023-08-11 18:09:14.000000,2023-08-11 04:50:09,595.0,78.0,64.0,11.0,88.0,23.0,23.0,144.0,2023-08-11 04:51:42,2023.08.01,40.0,7.0,alignn,,4.0,4.0,https://pypi.org/project/alignn,510.0,510.0,,,,2.0,,,,,,,,,,,,,,,,
+55,e3nn-jax,,rep-learn,Apache-2.0,https://github.com/e3nn/e3nn-jax,jax library for E3 Equivariant Neural Networks.,18,True,,e3nn/e3nn-jax,https://github.com/e3nn/e3nn-jax,2021-06-08 13:21:51,2023-08-11 13:46:24.000000,2023-08-11 13:46:20,894.0,63.0,13.0,9.0,29.0,1.0,7.0,121.0,2023-06-24 17:06:31,0.19.3,35.0,4.0,e3nn-jax,,,,https://pypi.org/project/e3nn-jax,2243.0,2243.0,,,,2.0,,,,,,,,,,,,,,,,
+56,CHGNet,,mliap,https://github.com/CederGroupHub/chgnet/blob/main/LICENSE,https://github.com/CederGroupHub/chgnet,Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov.,18,True,"['md', 'pre-trained', 'electrostatics', 'magnetism', 'structure-relaxation']",CederGroupHub/chgnet,https://github.com/CederGroupHub/chgnet,2023-02-24 23:44:24,2023-08-24 03:37:11.000000,2023-08-24 03:36:33,277.0,92.0,16.0,4.0,47.0,1.0,14.0,79.0,2023-07-22 02:21:49,0.2.0,6.0,5.0,chgnet,,1.0,1.0,https://pypi.org/project/chgnet,10276.0,10276.0,,,,2.0,,,,,,,,,True,,,,,,,
+57,Scikit-Matter,,general-tool,BSD-3-Clause,https://github.com/scikit-learn-contrib/scikit-matter,A collection of scikit-learn compatible utilities that implement methods born out of the materials science and..,18,True,['scikit-learn'],scikit-learn-contrib/scikit-matter,https://github.com/scikit-learn-contrib/scikit-matter,2020-10-12 19:23:26,2023-08-25 09:05:56.000000,2023-08-25 09:05:38,362.0,35.0,15.0,17.0,147.0,12.0,56.0,60.0,2023-08-24 17:18:49,0.2.0,7.0,12.0,skmatter,conda-forge/skmatter,5.0,5.0,https://pypi.org/project/skmatter,330.0,330.0,https://anaconda.org/conda-forge/skmatter,,,2.0,,,,,,,,,,,,,,,,
+58,MALA,,ml-dft,BSD-3-Clause,https://github.com/mala-project/mala,Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data.,18,True,,mala-project/mala,https://github.com/mala-project/mala,2021-03-31 11:40:38,2023-08-04 14:05:18.000000,2023-08-04 14:04:12,2070.0,85.0,19.0,9.0,248.0,23.0,208.0,50.0,2022-10-18 07:04:37,1.1.0,7.0,41.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
+59,Uni-Mol,,rep-learn,MIT,https://github.com/dptech-corp/Uni-Mol,Official Repository for the Uni-Mol Series Methods.,17,True,['pre-trained'],dptech-corp/Uni-Mol,https://github.com/dptech-corp/Uni-Mol,2022-05-22 13:26:41,2023-08-25 09:28:29.000000,2023-08-25 08:28:15,81.0,10.0,72.0,19.0,56.0,27.0,67.0,398.0,2023-07-07 09:02:23,0.2,2.0,8.0,,,,,,,498.0,,,,2.0,,,,,,4981.0,,,,,,,,,,
+60,escnn,,rep-learn,https://github.com/QUVA-Lab/escnn/blob/master/LICENSE,https://github.com/QUVA-Lab/escnn,Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/.,17,True,,QUVA-Lab/escnn,https://github.com/QUVA-Lab/escnn,2022-03-16 10:15:02,2023-08-09 11:36:06.000000,2023-08-09 11:23:03,240.0,53.0,33.0,15.0,23.0,16.0,36.0,203.0,,,,9.0,escnn,,,,https://pypi.org/project/escnn,1174.0,1174.0,,,,2.0,,,,,,,,,True,,,,,,,
+61,MoLeR,,generative,MIT,https://github.com/microsoft/molecule-generation,Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation.,17,True,,microsoft/molecule-generation,https://github.com/microsoft/molecule-generation,2022-02-17 19:16:29,2023-08-09 14:17:04.000000,2023-08-09 14:17:01,63.0,4.0,31.0,11.0,33.0,6.0,24.0,199.0,2023-06-18 21:03:46,0.4.0,4.0,5.0,molecule-generation,,,,https://pypi.org/project/molecule-generation,184.0,184.0,,,,2.0,,,,,,,,,,,,,,,,
+62,KFAC-JAX,,math,Apache-2.0,https://github.com/deepmind/kfac-jax,Second Order Optimization and Curvature Estimation with K-FAC in JAX.,17,True,,deepmind/kfac-jax,https://github.com/deepmind/kfac-jax,2022-03-18 10:19:24,2023-08-23 12:47:49.000000,2023-08-22 17:11:59,140.0,20.0,10.0,6.0,151.0,4.0,4.0,151.0,2023-05-16 18:03:40,0.0.5,4.0,11.0,kfac-jax,,6.0,6.0,https://pypi.org/project/kfac-jax,604.0,604.0,,,,1.0,,,,,,,,,,,,,,,,
+63,Chemiscope,,visualization,BSD-3-Clause,https://github.com/lab-cosmo/chemiscope,An interactive structure/property explorer for materials and molecules.,17,True,['lang-js'],lab-cosmo/chemiscope,https://github.com/lab-cosmo/chemiscope,2019-10-03 09:59:42,2023-08-25 08:20:51.000000,2023-07-25 15:28:49,648.0,3.0,26.0,15.0,183.0,40.0,66.0,86.0,2023-03-15 15:39:40,0.5.2,12.0,17.0,,,5.0,5.0,,,40.0,,,,1.0,,,,,,135.0,,,,chemiscope,https://www.npmjs.com/package/chemiscope,37.0,,,,
+64,MAST-ML,,general-tool,MIT,https://github.com/uw-cmg/MAST-ML,MAterials Simulation Toolkit for Machine Learning (MAST-ML).,17,True,,uw-cmg/MAST-ML,https://github.com/uw-cmg/MAST-ML,2017-02-16 17:03:57,2023-07-28 19:11:53.000000,2023-07-28 18:33:43,3162.0,3.0,50.0,13.0,36.0,22.0,191.0,86.0,2023-05-01 21:32:25,3.1.7,6.0,19.0,,,6.0,6.0,,,2.0,,,,2.0,,,,,,81.0,,,,,,,,,,
+65,QML,,general-tool,MIT,https://github.com/qmlcode/qml,QML: Quantum Machine Learning.,16,False,,qmlcode/qml,https://github.com/qmlcode/qml,2017-04-22 04:48:38,2023-07-26 12:01:42.000000,2018-09-10 11:14:35,75.0,,78.0,23.0,101.0,27.0,19.0,185.0,,,3.0,2.0,qml,,18.0,18.0,https://pypi.org/project/qml,246.0,246.0,,,,2.0,,,,,,,,,,,,,,,,
+66,XenonPy,,general-tool,BSD-3-Clause,https://github.com/yoshida-lab/XenonPy,XenonPy is a Python Software for Materials Informatics.,16,True,,yoshida-lab/XenonPy,https://github.com/yoshida-lab/XenonPy,2018-01-17 10:13:29,2023-05-21 15:54:32.000000,2023-05-21 15:53:00,682.0,,57.0,12.0,182.0,16.0,66.0,107.0,2023-05-21 15:54:32,0.6.8,45.0,10.0,xenonpy,,,,https://pypi.org/project/xenonpy,165.0,182.0,,,,2.0,,,,,,1168.0,,,,,,,,,,
+67,gpax,,math,MIT,https://github.com/ziatdinovmax/gpax,Gaussian Processes for Experimental Sciences.,16,True,"['probabilistic', 'active-learning']",ziatdinovmax/gpax,https://github.com/ziatdinovmax/gpax,2021-10-28 13:43:18,2023-08-24 05:24:19.000000,2023-08-07 01:30:41,452.0,101.0,15.0,3.0,29.0,2.0,5.0,101.0,2023-07-09 23:50:56,0.0.7,7.0,,gpax,,,,https://pypi.org/project/gpax,129.0,129.0,,,,2.0,,,,,,,,,,,,,,,,
+68,CatLearn,,rep-eng,GPL-3.0,https://github.com/SUNCAT-Center/CatLearn,,16,True,['surface-science'],SUNCAT-Center/CatLearn,https://github.com/SUNCAT-Center/CatLearn,2018-04-20 04:16:14,2023-07-25 21:09:47.000000,2023-02-07 09:31:25,1960.0,,50.0,19.0,79.0,9.0,16.0,92.0,2020-03-27 09:26:03,0.6.2,8.0,22.0,catlearn,,4.0,4.0,https://pypi.org/project/catlearn,87.0,87.0,,,,1.0,,,,,,,,,,,,,,,,
+69,MatBench,,community,MIT,https://github.com/materialsproject/matbench,Matbench: Benchmarks for materials science property prediction.,16,True,"['datasets', 'benchmarking']",materialsproject/matbench,https://github.com/materialsproject/matbench,2021-02-24 03:58:42,2023-08-21 14:34:20.000000,2023-08-07 23:41:31,736.0,8.0,30.0,9.0,222.0,31.0,26.0,77.0,2022-07-27 04:40:26,0.6,5.0,22.0,matbench,,9.0,9.0,https://pypi.org/project/matbench,145.0,145.0,,,,2.0,,,,,,,,,True,,,,,,,
+70,benchmarking-gnns,,rep-learn,MIT,https://github.com/graphdeeplearning/benchmarking-gnns,Repository for benchmarking graph neural networks.,15,False,"['single-paper', 'benchmarking']",graphdeeplearning/benchmarking-gnns,https://github.com/graphdeeplearning/benchmarking-gnns,2020-03-03 03:42:50,2023-06-22 04:03:53.000000,2022-05-10 13:22:20,45.0,,415.0,59.0,17.0,4.0,60.0,2234.0,,,,5.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+71,FermiNet,,ml-wft,Apache-2.0,https://github.com/deepmind/ferminet,An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations.,15,True,,deepmind/ferminet,https://github.com/deepmind/ferminet,2020-10-06 12:21:06,2023-08-14 14:54:45.000000,2023-08-14 13:41:50,192.0,12.0,98.0,34.0,27.0,,39.0,576.0,,,,18.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+72,Uni-Fold,,biomolecules,Apache-2.0,https://github.com/dptech-corp/Uni-Fold,An open-source platform for developing protein models beyond AlphaFold.,15,True,,dptech-corp/Uni-Fold,https://github.com/dptech-corp/Uni-Fold,2022-07-30 03:37:29,2023-08-23 10:48:55.000000,2023-06-19 13:56:13,95.0,2.0,47.0,7.0,72.0,9.0,47.0,283.0,2022-10-19 12:44:31,2.2.0,3.0,7.0,,,,,,,213.0,,,,3.0,,,,,,2130.0,,,,,,,,,,
+73,sGDML,,mliap,MIT,https://github.com/stefanch/sGDML,sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model.,15,True,,stefanch/sGDML,https://github.com/stefanch/sGDML,2018-07-11 15:20:30,2023-08-07 08:17:29.000000,2023-06-08 12:49:46,199.0,1.0,34.0,9.0,12.0,5.0,11.0,123.0,2023-06-08 12:51:43,1.0.1,14.0,7.0,sgdml,,8.0,8.0,https://pypi.org/project/sgdml,108.0,108.0,,,,2.0,,,,,,,,,,,,,,,,
+74,PyXtalFF,,mliap,MIT,https://github.com/MaterSim/PyXtal_FF,Machine Learning Interatomic Potential Predictions.,15,True,,MaterSim/PyXtal_FF,https://github.com/MaterSim/PyXtal_FF,2019-01-08 08:43:35,2023-08-17 01:22:23.000000,2023-08-17 01:22:18,559.0,6.0,19.0,9.0,2.0,9.0,51.0,71.0,2023-06-09 17:17:24,0.2.3,19.0,8.0,pyxtal_ff,,,,https://pypi.org/project/pyxtal_ff,48.0,48.0,,,,2.0,,,,,,,,,,,,,,,,
+75,DADApy,,unsupervised,Apache-2.0,https://github.com/sissa-data-science/DADApy,Distance-based Analysis of DAta-manifolds in python.,15,True,,sissa-data-science/DADApy,https://github.com/sissa-data-science/DADApy,2021-02-16 17:45:23,2023-08-15 08:50:41.000000,2023-08-15 08:50:41,643.0,12.0,8.0,8.0,73.0,9.0,15.0,69.0,2023-05-25 16:37:17,0.2.0,3.0,15.0,dadapy,,2.0,2.0,https://pypi.org/project/dadapy,37.0,37.0,,,,1.0,,,,,,,,,,,,,,,,
+76,NNPOps,,mliap,MIT,https://github.com/openmm/NNPOps,High-performance operations for neural network potentials.,15,True,"['md', 'lang-cpp']",openmm/NNPOps,https://github.com/openmm/NNPOps,2020-09-10 21:02:00,2023-08-18 15:51:48.000000,2023-07-25 21:23:53,94.0,3.0,12.0,9.0,61.0,20.0,32.0,61.0,2023-07-26 11:21:58,0.6,7.0,7.0,,conda-forge/nnpops,,,,,3774.0,https://anaconda.org/conda-forge/nnpops,2023-08-17 11:03:53.238,64171.0,2.0,,,,,,,,,True,,,,,,,
+77,AI for Science Resources,,community,GPL-3.0 license,https://github.com/divelab/AIRS/blob/main/Overview/resources.md,"List of resources for AI4Science research, including learning resources.",14,True,,divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2023-08-24 19:43:02.000000,2023-08-24 19:43:02,322.0,302.0,25.0,17.0,4.0,,2.0,218.0,,,,23.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,
+78,Artificial Intelligence for Science (AIRS),,general-tool,GPL-3.0 license,https://github.com/divelab/AIRS,Artificial Intelligence for Science (AIRS).,14,True,"['rep-learn', 'generative', 'mliap', 'md', 'ml-dft', 'ml-wft', 'biomolecules']",divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2023-08-24 19:43:02.000000,2023-08-24 19:43:02,322.0,302.0,25.0,17.0,4.0,,2.0,218.0,,,,23.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,
+79,MACE,,mliap,MIT,https://github.com/ACEsuit/mace,MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.,14,True,,ACEsuit/mace,https://github.com/ACEsuit/mace,2022-06-21 18:44:34,2023-08-22 09:08:41.000000,2023-08-17 09:53:26,210.0,3.0,61.0,18.0,72.0,16.0,38.0,184.0,2023-02-09 12:24:53,0.2.0,1.0,11.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+80,SISSO,,rep-eng,Apache-2.0,https://github.com/rouyang2017/SISSO,A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models.,14,True,['lang-fortran'],rouyang2017/SISSO,https://github.com/rouyang2017/SISSO,2017-10-16 11:31:57,2023-07-26 04:28:51.000000,2023-07-26 04:28:51,164.0,14.0,60.0,6.0,2.0,,50.0,175.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+81,openmm-torch,,md,https://github.com/openmm/openmm-torch#license,https://github.com/openmm/openmm-torch,OpenMM plugin to define forces with neural networks.,14,True,"['mliap', 'lang-cpp']",openmm/openmm-torch,https://github.com/openmm/openmm-torch,2019-09-27 18:15:19,2023-07-27 15:41:41.596000,2023-07-27 15:33:41,56.0,2.0,23.0,12.0,51.0,16.0,48.0,130.0,2023-07-26 13:16:26,1.1,13.0,6.0,,conda-forge/openmm-torch,,,,,6714.0,https://anaconda.org/conda-forge/openmm-torch,2023-07-27 15:41:41.596,208164.0,2.0,,,,,,,,,True,,,,,,,
+82,Automatminer,,general-tool,https://github.com/hackingmaterials/automatminer/blob/main/LICENSE,https://github.com/hackingmaterials/automatminer,An automatic engine for predicting materials properties.,14,False,,hackingmaterials/automatminer,https://github.com/hackingmaterials/automatminer,2018-05-10 18:27:08,2022-12-07 10:00:21.000000,2022-01-06 19:39:49,1666.0,,43.0,12.0,232.0,36.0,138.0,125.0,2020-07-28 02:19:07,1.0.3.20200727,17.0,13.0,automatminer,,6.0,6.0,https://pypi.org/project/automatminer,80.0,80.0,,,,3.0,,,,,,,,,,,,,,,,
+83,SPICE,,datasets,MIT,https://github.com/openmm/spice-dataset,A collection of QM data for training potential functions.,14,True,"['mliap', 'md']",openmm/spice-dataset,https://github.com/openmm/spice-dataset,2021-08-31 18:52:05,2023-08-18 21:02:41.000000,2023-08-18 21:02:41,31.0,10.0,4.0,17.0,34.0,11.0,34.0,89.0,2023-08-07 19:52:03,1.1.4,6.0,,,,,,,,15.0,,,,2.0,,,,,,202.0,,,True,,,,,,,
+84,SpheriCart,,math,Apache-2.0,https://github.com/lab-cosmo/sphericart,Multi-language library for the calculation of spherical harmonics in Cartesian coordinates.,14,True,,lab-cosmo/sphericart,https://github.com/lab-cosmo/sphericart,2023-02-04 15:15:25,2023-08-18 12:23:31.000000,2023-08-18 12:20:46,317.0,11.0,3.0,4.0,53.0,8.0,6.0,40.0,2023-04-26 12:06:09,0.3.0,1.0,6.0,sphericart,,1.0,1.0,https://pypi.org/project/sphericart,25.0,25.0,,,,2.0,,,,,,,,,,,,,,,,
+85,Ultra-Fast Force Fields (UF3),,mliap,Apache-2.0,https://github.com/uf3/uf3,UF3: a python library for generating ultra-fast interatomic potentials.,14,True,,uf3/uf3,https://github.com/uf3/uf3,2021-10-01 13:21:44,2023-08-24 18:39:48.000000,2023-08-24 18:39:48,462.0,31.0,14.0,4.0,30.0,10.0,17.0,30.0,2022-09-29 10:22:37,0.3.2,3.0,6.0,uf3,,,,https://pypi.org/project/uf3,25.0,25.0,,,,2.0,,,,,,,,,,,,,,,,
+86,KLIFF,,mliap,LGPL-2.1,https://github.com/openkim/kliff,KIM-based Learning-Integrated Fitting Framework (KLIFF).,14,True,"['probabilistic', 'workflows']",openkim/kliff,https://github.com/openkim/kliff,2017-08-01 20:33:58,2023-08-14 00:02:14.000000,2023-08-09 17:09:55,982.0,31.0,17.0,2.0,96.0,17.0,16.0,27.0,2022-10-07 05:16:11,0.4.1,16.0,10.0,kliff,conda-forge/kliff,,,https://pypi.org/project/kliff,90.0,1484.0,https://anaconda.org/conda-forge/kliff,2023-06-16 16:19:12.982,62771.0,2.0,,,,,,,,,,,,,,,,
+87,mp-pyrho,,data-structures,https://github.com/materialsproject/pyrho,https://github.com/materialsproject/pyrho,,14,False,['ml-dft'],materialsproject/pyrho,https://github.com/materialsproject/pyrho,2020-05-25 22:44:02,2023-08-14 00:12:07.000000,2023-03-21 17:22:18,244.0,,6.0,9.0,104.0,1.0,3.0,27.0,2022-10-20 05:07:16,0.3.0,23.0,8.0,mp-pyrho,,18.0,18.0,https://pypi.org/project/mp-pyrho,273.0,273.0,,,,3.0,,,,,,,,,True,,,,,,,
+88,Polynomials4ML.jl,,math,MIT,https://github.com/ACEsuit/Polynomials4ML.jl,"Polynomials for ML: fast evaluation, batching, differentiation.",14,True,['lang-julia'],ACEsuit/Polynomials4ML.jl,https://github.com/ACEsuit/Polynomials4ML.jl,2022-09-20 23:05:53,2023-08-09 23:54:30.000000,2023-08-09 05:03:18,278.0,134.0,2.0,3.0,24.0,16.0,22.0,10.0,2023-07-29 00:42:23,0.2.2,6.0,8.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+89,n2p2,,mliap,GPL-3.0,https://github.com/CompPhysVienna/n2p2,n2p2 - A Neural Network Potential Package.,13,True,['lang-cpp'],CompPhysVienna/n2p2,https://github.com/CompPhysVienna/n2p2,2018-07-25 12:29:17,2023-05-11 16:26:02.000000,2022-09-05 10:56:20,387.0,,66.0,12.0,53.0,54.0,85.0,185.0,2022-05-23 12:53:39,2.2.0,11.0,9.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+90,DeepH-pack,,ml-dft,LGPL-3.0,https://github.com/mzjb/DeepH-pack,Deep neural networks for density functional theory Hamiltonian.,13,True,['lang-julia'],mzjb/DeepH-pack,https://github.com/mzjb/DeepH-pack,2022-05-13 02:51:32,2023-08-03 05:36:54.000000,2023-07-11 08:11:15,54.0,9.0,25.0,4.0,13.0,5.0,29.0,130.0,2023-07-11 08:13:06,0.2.2,2.0,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+91,AMPtorch,,general-tool,GPL-3.0,https://github.com/ulissigroup/amptorch,AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch.,13,True,,ulissigroup/amptorch,https://github.com/ulissigroup/amptorch,2019-01-24 15:15:48,2023-07-16 02:11:38.000000,2023-07-16 02:08:13,759.0,7.0,31.0,9.0,99.0,4.0,25.0,53.0,2023-07-16 02:11:38,1.0,3.0,14.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+92,So3krates (MLFF),,mliap,MIT,https://github.com/thorben-frank/mlff,Build neural networks for machine learning force fields with JAX.,13,True,,thorben-frank/mlff,https://github.com/thorben-frank/mlff,2022-09-30 07:40:17,2023-08-18 06:21:23.000000,2023-08-18 06:21:16,97.0,60.0,6.0,3.0,9.0,1.0,4.0,35.0,2023-07-12 08:34:56,0.2.0,1.0,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+93,aviary,,materials-discovery,MIT,https://github.com/CompRhys/aviary,The Wren sits on its Roost in the Aviary.,13,True,,CompRhys/aviary,https://github.com/CompRhys/aviary,2021-09-28 12:29:05,2023-08-10 01:55:31.000000,2023-08-10 01:50:53,603.0,5.0,7.0,2.0,48.0,4.0,22.0,27.0,2023-08-10 01:55:58,0.1.1,4.0,4.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
+94,Equistore,,data-structures,BSD-3-Clause,https://github.com/lab-cosmo/equistore,Storage format for equivariant atomistic machine learning.,13,True,,lab-cosmo/equistore,https://github.com/lab-cosmo/equistore,2022-03-01 15:58:28,2023-08-25 14:04:33.000000,2023-08-25 13:57:09,363.0,90.0,12.0,15.0,242.0,40.0,60.0,24.0,,,,16.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+95,GlassPy,,rep-eng,GPL-3.0,https://github.com/drcassar/glasspy,Python module for scientists working with glass materials.,13,True,,drcassar/glasspy,https://github.com/drcassar/glasspy,2019-07-18 23:15:43,2023-08-21 11:47:19.000000,2023-08-21 11:46:55,311.0,19.0,6.0,4.0,7.0,2.0,1.0,14.0,2023-08-18 11:55:53,0.4.3,5.0,,glasspy,,2.0,2.0,https://pypi.org/project/glasspy,208.0,208.0,,,,2.0,,,,,,,,,True,,,,,,,
+96,CCS_fit,,mliap,GPL-3.0,https://github.com/Teoroo-CMC/CCS,Curvature Constrained Splines.,13,True,,Teoroo-CMC/CCS,https://github.com/Teoroo-CMC/CCS,2021-12-13 14:29:53,2023-08-25 13:54:02.000000,2023-08-25 13:54:01,755.0,24.0,8.0,2.0,10.0,8.0,6.0,5.0,2023-08-25 13:54:04,0.22.2,100.0,8.0,ccs_fit,,,,https://pypi.org/project/ccs_fit,295.0,336.0,,,,2.0,,,,,,373.0,,,,,,,,,,
+97,mat2vec,,lm,MIT,https://github.com/materialsintelligence/mat2vec,Supplementary Materials for Tshitoyan et al. Unsupervised word embeddings capture latent knowledge from materials..,12,True,['rep-learn'],materialsintelligence/mat2vec,https://github.com/materialsintelligence/mat2vec,2019-04-25 07:55:30,2023-05-06 22:45:49.000000,2023-05-06 22:45:49,55.0,,170.0,40.0,7.0,6.0,17.0,593.0,,,,5.0,,,,,,,,,,,2.0,,,,,,,,,True,,,,,,,
+98,Deep Learning for Molecules and Materials Book,,educational,https://github.com/whitead/dmol-book/blob/main/LICENSE,https://dmol.pub/,Deep learning for molecules and materials book.,12,True,,whitead/dmol-book,https://github.com/whitead/dmol-book,2020-08-19 19:24:32,2023-07-02 18:02:57.000000,2023-07-02 18:02:56,558.0,3.0,96.0,17.0,92.0,25.0,128.0,520.0,,,,19.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
+99,Crystal Graph Convolutional Neural Networks (CGCNN),,rep-learn,MIT,https://github.com/txie-93/cgcnn,Crystal graph convolutional neural networks for predicting material properties.,12,False,,txie-93/cgcnn,https://github.com/txie-93/cgcnn,2018-03-14 20:41:21,2021-09-06 05:23:51.000000,2021-09-06 05:23:38,25.0,,247.0,22.0,7.0,15.0,18.0,496.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+100,TensorMol,,mliap,GPL-3.0,https://github.com/jparkhill/TensorMol,Tensorflow + Molecules = TensorMol.,12,False,['single-paper'],jparkhill/TensorMol,https://github.com/jparkhill/TensorMol,2016-10-28 19:40:11,2021-02-11 00:12:00.000000,2018-03-30 12:26:14,1724.0,,70.0,45.0,8.0,18.0,19.0,261.0,2017-11-08 18:05:50,0.1,1.0,12.0,,,,,,,,,,,2.0,,,,,,,,,True,,,,,,,
+101,ASAP,,unsupervised,MIT,https://github.com/BingqingCheng/ASAP,ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.,12,True,,BingqingCheng/ASAP,https://github.com/BingqingCheng/ASAP,2019-08-11 12:45:14,2023-07-25 07:38:32.000000,2023-07-25 07:32:45,758.0,3.0,25.0,7.0,37.0,6.0,18.0,113.0,,,,6.0,,,4.0,4.0,,,,,,,2.0,,,,,,,,,,,,,,,,
+102,DMFF,,mliap,LGPL-3.0,https://github.com/deepmodeling/DMFF,DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable..,12,True,,deepmodeling/DMFF,https://github.com/deepmodeling/DMFF,2022-02-14 01:35:50,2023-08-25 09:46:37.000000,2023-02-14 05:57:53,273.0,,27.0,12.0,96.0,9.0,5.0,108.0,2022-12-02 14:41:23,0.2.0,3.0,7.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+103,Librascal,,rep-eng,LGPL-2.1,https://github.com/lab-cosmo/librascal,A scalable and versatile library to generate representations for atomic-scale learning.,12,True,,lab-cosmo/librascal,https://github.com/lab-cosmo/librascal,2018-02-01 08:38:51,2023-06-06 10:01:25.000000,2023-06-06 10:01:20,2930.0,2.0,18.0,19.0,198.0,99.0,131.0,70.0,,,3.0,29.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+104,Neural fingerprint (nfp),,rep-learn,https://github.com/NREL/nfp/blob/master/LICENSE,https://github.com/NREL/nfp,Keras layers for end-to-end learning with rdkit and pymatgen.,12,False,,NREL/nfp,https://github.com/NREL/nfp,2018-11-20 23:55:23,2022-08-29 21:59:14.000000,2022-06-14 22:18:28,143.0,,24.0,7.0,18.0,,6.0,53.0,2022-04-27 17:05:25,0.3.12,13.0,4.0,,,12.0,12.0,,,,,,,2.0,,,,,,,,,,,,,,,,
+105,Pacemaker,,mliap,https://github.com/ICAMS/python-ace/blob/master/LICENSE.md,https://cortner.github.io/ACEweb/software/,Python package for fitting atomic cluster expansion (ACE) potentials.,12,True,,ICAMS/python-ace,https://github.com/ICAMS/python-ace,2021-11-19 11:39:54,2023-07-21 17:51:03.000000,2023-07-21 17:50:02,113.0,16.0,9.0,3.0,20.0,6.0,25.0,42.0,2022-10-24 19:59:33,0.2.8,2.0,5.0,python-ace,,,,https://pypi.org/project/python-ace,6.0,6.0,,,,2.0,,,,,,,,,,,,,,,,
+106,jarvis-tools-notebooks,,educational,NIST,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/.,12,True,,JARVIS-Materials-Design/jarvis-tools-notebooks,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,2020-06-27 20:22:02,2023-08-19 17:55:57.000000,2023-08-19 17:55:56,499.0,188.0,21.0,3.0,37.0,,,37.0,,,,5.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
+107,synspace,,generative,MIT,https://github.com/whitead/synspace,Synthesis generative model.,12,True,,whitead/synspace,https://github.com/whitead/synspace,2022-12-28 00:59:14,2023-04-15 22:42:57.000000,2023-04-15 18:04:16,27.0,,3.0,3.0,1.0,2.0,1.0,29.0,2023-04-15 22:42:57,0.3.0,3.0,2.0,synspace,,4.0,4.0,https://pypi.org/project/synspace,332.0,332.0,,,,2.0,,,,,,,,,,,,,,,,
+108,Compositionally-Restricted Attention-Based Network (CrabNet),,rep-learn,MIT,https://github.com/sparks-baird/CrabNet,Predict materials properties using only the composition information!.,12,True,,sparks-baird/CrabNet,https://github.com/sparks-baird/CrabNet,2021-09-17 07:58:15,2023-06-19 09:35:52.000000,2023-06-19 09:35:52,427.0,1.0,3.0,1.0,54.0,14.0,2.0,10.0,2023-06-07 01:07:33,2.0.8,5.0,5.0,crabnet,,10.0,10.0,https://pypi.org/project/crabnet,108.0,108.0,,,,2.0,,,,,,,,,,,,,,,,
+109,OpenChem,,general-tool,MIT,https://github.com/Mariewelt/OpenChem,OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research.,11,False,,Mariewelt/OpenChem,https://github.com/Mariewelt/OpenChem,2018-07-10 01:27:33,2023-06-06 01:57:19.000000,2022-04-27 19:27:40,444.0,,104.0,37.0,11.0,15.0,2.0,613.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+110,ReLeaSE,,reinforcement-learning,MIT,https://github.com/isayev/ReLeaSE,Deep Reinforcement Learning for de-novo Drug Design.,11,False,['drug-discovery'],isayev/ReLeaSE,https://github.com/isayev/ReLeaSE,2018-04-26 14:50:34,2021-12-08 19:49:36.000000,2021-12-08 19:49:36,160.0,,121.0,19.0,9.0,27.0,8.0,307.0,,,,5.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
+111,DeepLearningLifeSciences,,educational,MIT,https://github.com/deepchem/DeepLearningLifeSciences,Example code from the book Deep Learning for the Life Sciences.,11,False,,deepchem/DeepLearningLifeSciences,https://github.com/deepchem/DeepLearningLifeSciences,2019-02-05 17:16:18,2021-09-17 05:10:37.000000,2021-09-17 05:10:37,52.0,,133.0,23.0,15.0,10.0,8.0,298.0,2019-10-28 18:46:28,1.0,1.0,10.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+112,Awesome Materials Informatics,,community,https://github.com/tilde-lab/awesome-materials-informatics#license,https://github.com/tilde-lab/awesome-materials-informatics,Curated list of known efforts in materials informatics = modern materials science.,11,True,,tilde-lab/awesome-materials-informatics,https://github.com/tilde-lab/awesome-materials-informatics,2018-02-15 15:14:16,2023-08-21 21:56:00.000000,2023-08-21 21:56:00,130.0,9.0,72.0,15.0,53.0,,8.0,294.0,2023-03-02 19:56:59,2023.03.02,1.0,18.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+113,ANI-1,,mliap,MIT,https://github.com/isayev/ASE_ANI,ANI-1 neural net potential with python interface (ASE).,11,False,,isayev/ASE_ANI,https://github.com/isayev/ASE_ANI,2016-12-08 05:09:32,2020-12-14 19:57:50.000000,2020-06-05 22:46:43,111.0,,55.0,36.0,9.0,12.0,20.0,202.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+114,Neural Force Field,,mliap,MIT,https://github.com/learningmatter-mit/NeuralForceField,Neural Network Force Field based on PyTorch.,11,True,['pre-trained'],learningmatter-mit/NeuralForceField,https://github.com/learningmatter-mit/NeuralForceField,2020-10-04 15:17:41,2023-07-25 15:37:02.000000,2023-07-25 15:37:01,122.0,4.0,41.0,8.0,4.0,,16.0,183.0,,,,10.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+115,gptchem,,lm,MIT,https://github.com/kjappelbaum/gptchem,Use GPT-3 to solve chemistry problems.,11,True,,kjappelbaum/gptchem,https://github.com/kjappelbaum/gptchem,2023-01-06 15:34:32,2023-06-22 05:28:27.000000,2023-06-22 05:28:27,108.0,1.0,27.0,5.0,2.0,3.0,2.0,158.0,2023-04-06 19:44:55,0.0.1,1.0,2.0,gptchem,,,,https://pypi.org/project/gptchem,128.0,128.0,,,,2.0,,,,,,,,,,,,,,,,
+116,PiNN,,mliap,BSD-3-Clause,https://github.com/Teoroo-CMC/PiNN,A Python library for building atomic neural networks.,11,True,,Teoroo-CMC/PiNN,https://github.com/Teoroo-CMC/PiNN,2019-10-04 08:13:18,2023-08-23 09:38:55.030628,2023-08-18 07:44:37,124.0,11.0,25.0,6.0,1.0,2.0,4.0,94.0,2019-10-09 09:21:30,0.3.0,1.0,2.0,,,,,,,4.0,,,,2.0,teoroo/pinn,https://hub.docker.com/r/teoroo/pinn,2023-08-23 09:38:55.030628,,215.0,,,,,,,,,,,
+117,MolSkill,,lm,MIT,https://github.com/microsoft/molskill,Learning chemical intuition from humans in the loop. Supporting code.,11,True,"['drug-discovery', 'recommender']",microsoft/molskill,https://github.com/microsoft/molskill,2023-01-12 13:48:31,2023-08-04 12:22:15.000000,2023-06-13 09:58:31,80.0,3.0,5.0,8.0,8.0,2.0,3.0,77.0,2023-08-04 12:22:15,1.2b,5.0,4.0,,msr-ai4science/molskill,,,,,14.0,https://anaconda.org/msr-ai4science/molskill,2023-06-18 17:27:43.196,89.0,2.0,,,,,,,,,,,,,,,,
+118,nlcc,,lm,MIT,https://github.com/whitead/nlcc,Natural language computational chemistry command line interface.,11,True,['single-paper'],whitead/nlcc,https://github.com/whitead/nlcc,2021-08-19 18:23:52,2023-02-04 03:07:56.000000,2023-02-04 03:06:33,144.0,,6.0,4.0,1.0,,9.0,41.0,2023-02-04 03:07:56,0.6.0,10.0,3.0,nlcc,,,,https://pypi.org/project/nlcc,49.0,49.0,,,,2.0,,,,,,,,,,,,,,,,
+119,SIMPLE-NN,,mliap,GPL-3.0,https://github.com/MDIL-SNU/SIMPLE-NN,SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network).,11,False,,MDIL-SNU/SIMPLE-NN,https://github.com/MDIL-SNU/SIMPLE-NN,2018-03-26 23:53:35,2022-01-27 05:04:05.000000,2022-01-27 05:04:05,586.0,,18.0,12.0,91.0,4.0,26.0,41.0,2021-09-23 01:41:42,1.1.1,9.0,4.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+120,OpenKIM,,datasets,LGPL-2.1,https://openkim.org/,"The Open Knowledgebase of Interatomic Models (OpenKIM) aims to be an online resource for standardized testing, long-..",11,False,"['knowledge-base', 'pre-trained']",openkim/kim-api,https://github.com/openkim/kim-api,2014-07-28 21:21:08,2023-08-16 00:09:44.000000,2022-03-17 23:01:36,2371.0,,19.0,11.0,55.0,17.0,18.0,29.0,,,,23.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+121,SchNetPack G-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/schnetpack-gschnet,G-SchNet extension for SchNetPack.,11,True,,atomistic-machine-learning/schnetpack-gschnet,https://github.com/atomistic-machine-learning/schnetpack-gschnet,2022-04-21 12:34:13,2023-06-01 14:11:50.000000,2023-06-01 14:11:46,116.0,1.0,3.0,4.0,,,8.0,26.0,2023-04-25 14:09:07,1.0.0,2.0,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+122,Rascaline,,rep-eng,BSD-3-Clause,https://github.com/Luthaf/rascaline,Computing representations for atomistic machine learning.,11,True,"['lang-rust', 'lang-cpp']",Luthaf/rascaline,https://github.com/Luthaf/rascaline,2020-09-24 14:28:34,2023-08-24 16:57:37.000000,2023-08-22 14:40:47,446.0,58.0,11.0,6.0,186.0,18.0,18.0,13.0,,,,10.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+123,ACEfit,,mliap,MIT,https://github.com/ACEsuit/ACEfit.jl,,11,False,['lang-julia'],ACEsuit/ACEfit.jl,https://github.com/ACEsuit/ACEfit.jl,2022-01-01 00:09:17,2023-08-18 16:52:10.000000,2023-08-18 16:46:11,210.0,36.0,3.0,3.0,14.0,21.0,32.0,4.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+124,SE(3)-Transformers,,rep-learn,MIT,https://github.com/FabianFuchsML/se3-transformer-public,code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503.,10,False,['single-paper'],FabianFuchsML/se3-transformer-public,https://github.com/FabianFuchsML/se3-transformer-public,2020-08-31 10:36:57,2023-07-10 05:13:25.000000,2021-11-18 09:11:56,63.0,,62.0,15.0,5.0,9.0,17.0,408.0,,,,2.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+125,GDC,,rep-learn,MIT,https://github.com/gasteigerjo/gdc,"Graph Diffusion Convolution, as proposed in Diffusion Improves Graph Learning (NeurIPS 2019).",10,True,['generative'],gasteigerjo/gdc,https://github.com/gasteigerjo/gdc,2019-10-26 16:05:11,2023-04-26 14:22:40.000000,2023-04-26 14:22:40,28.0,,38.0,3.0,1.0,,10.0,219.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+126,DeePKS-kit,,ml-dft,LGPL-3.0,https://github.com/deepmodeling/deepks-kit,a package for developing machine learning-based chemically accurate energy and density functional models.,10,True,,deepmodeling/deepks-kit,https://github.com/deepmodeling/deepks-kit,2020-07-29 03:27:50,2023-08-24 00:29:24.000000,2023-04-01 01:14:46,380.0,,29.0,12.0,39.0,1.0,9.0,93.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+127,OpenMM-ML,,md,MIT,https://github.com/openmm/openmm-ml,High level API for using machine learning models in OpenMM simulations.,10,True,['mliap'],openmm/openmm-ml,https://github.com/openmm/openmm-ml,2021-02-10 20:55:25,2023-08-21 23:33:29.497000,2023-08-21 20:11:13,28.0,2.0,13.0,14.0,19.0,21.0,18.0,50.0,2023-08-21 23:32:24,1.1,5.0,2.0,,conda-forge/openmm-ml,,,,,134.0,https://anaconda.org/conda-forge/openmm-ml,2023-08-21 23:33:29.497,1613.0,3.0,,,,,,,,,True,,,,,,,
+128,flare++,,active-learning,MIT,https://github.com/mir-group/flare_pp,A many-body extension of the FLARE code.,10,False,"['lang-cpp', 'mliap']",mir-group/flare_pp,https://github.com/mir-group/flare_pp,2019-11-20 22:46:32,2022-02-27 21:05:09.000000,2022-02-24 19:00:50,989.0,,6.0,6.0,28.0,8.0,17.0,34.0,,,,10.0,flare_pp,,,,https://pypi.org/project/flare_pp,129.0,129.0,,,,2.0,,,,,,,,,,,,,,,,
+129,cmlkit,,rep-eng,MIT,https://github.com/sirmarcel/cmlkit,tools for machine learning in condensed matter physics and quantum chemistry.,10,False,['benchmarking'],sirmarcel/cmlkit,https://github.com/sirmarcel/cmlkit,2018-05-31 07:56:52,2022-04-01 00:39:14.000000,2022-03-25 22:27:04,526.0,,6.0,3.0,1.0,6.0,2.0,29.0,,,,,cmlkit,,4.0,4.0,https://pypi.org/project/cmlkit,35.0,35.0,,,,2.0,,,,,,,,,,,,,,,,
+130,NeuralXC,,ml-dft,BSD-3-Clause,https://github.com/semodi/neuralxc,Implementation of a machine learned density functional.,10,False,,semodi/neuralxc,https://github.com/semodi/neuralxc,2019-03-14 18:13:40,2022-11-30 11:39:22.000000,2021-07-05 21:36:23,337.0,,8.0,5.0,9.0,5.0,5.0,28.0,,,3.0,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+131,Finetuna,,active-learning,MIT,https://github.com/ulissigroup/finetuna,Active Learning for Machine Learning Potentials.,10,True,,ulissigroup/finetuna,https://github.com/ulissigroup/finetuna,2020-09-22 14:39:52,2023-03-15 18:08:52.000000,2023-02-13 20:10:48,1196.0,,6.0,3.0,37.0,3.0,14.0,25.0,,,,11.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+132,graphite,,rep-learn,MIT,https://github.com/LLNL/graphite,A repository for implementing graph network models based on atomic structures.,10,True,,llnl/graphite,https://github.com/LLNL/graphite,2022-06-27 19:15:27,2023-08-16 20:32:27.000000,2023-08-16 20:32:27,22.0,14.0,4.0,6.0,4.0,1.0,,21.0,,,,2.0,,,6.0,6.0,,,,,,,2.0,,,,,,,,,,,,,,,,
+133,ACE1.jl,,mliap,https://github.com/ACEsuit/ACE1.jl/blob/main/ASL.md,https://acesuit.github.io/,Atomic Cluster Expansion for Modelling Invariant Atomic Properties.,10,True,['lang-julia'],ACEsuit/ACE1.jl,https://github.com/ACEsuit/ACE1.jl,2022-01-14 19:52:49,2023-08-22 07:18:10.000000,2023-08-22 07:18:07,546.0,22.0,4.0,5.0,28.0,21.0,24.0,18.0,,,,7.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+134,NNsforMD,,mliap,MIT,https://github.com/aimat-lab/NNsForMD,"Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings.",10,True,,aimat-lab/NNsForMD,https://github.com/aimat-lab/NNsForMD,2020-08-31 11:14:18,2022-11-10 13:04:49.000000,2022-11-10 13:04:45,265.0,,3.0,3.0,,,,9.0,2022-04-12 15:10:32,2.0.0,5.0,2.0,pyNNsMD,,,,https://pypi.org/project/pyNNsMD,33.0,33.0,,,,3.0,,,,,,,,,,,,,,,,
+135,ai4material_design,,rep-learn,Apache-2.0,https://github.com/HSE-LAMBDA/ai4material_design,"Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of..",10,False,"['pre-trained', 'material-defect']",HSE-LAMBDA/ai4material_design,https://github.com/HSE-LAMBDA/ai4material_design,2021-03-25 10:06:20,2023-08-25 08:57:39.000000,2023-08-25 08:57:30,1106.0,75.0,,7.0,28.0,1.0,11.0,1.0,,,,11.0,,,,,,,,,,,2.0,,,,,,,,,True,,,,,,,
+136,ML4pXRDs,,rep-learn,MIT,https://github.com/aimat-lab/ML4pXRDs,Contains code to train neural networks based on simulated powder XRDs from synthetic crystals.,10,True,"['xrd', 'single-paper']",aimat-lab/ML4pXRDs,https://github.com/aimat-lab/ML4pXRDs,2022-12-01 16:24:29,2023-07-14 08:17:06.000000,2023-07-14 08:17:04,1320.0,21.0,1.0,3.0,,,,,2023-03-22 11:04:31,1.0,1.0,,,,,,,,0.0,,,,2.0,,,,,,2.0,,,,,,,,,,
+137,EDM,,generative,MIT,https://github.com/ehoogeboom/e3_diffusion_for_molecules,E(3) Equivariant Diffusion Model for Molecule Generation in 3D.,9,False,,ehoogeboom/e3_diffusion_for_molecules,https://github.com/ehoogeboom/e3_diffusion_for_molecules,2022-04-15 14:34:35,2022-07-10 17:56:18.000000,2022-07-10 17:56:12,6.0,,69.0,8.0,,14.0,12.0,287.0,,,,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,
+138,molecularGNN_smiles,,rep-learn,Apache-2.0,https://github.com/masashitsubaki/molecularGNN_smiles,"The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius..",9,False,,masashitsubaki/molecularGNN_smiles,https://github.com/masashitsubaki/molecularGNN_smiles,2018-11-06 00:25:26,2020-11-28 02:04:45.000000,2020-11-28 02:04:45,79.0,,68.0,6.0,,6.0,1.0,246.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+139,DimeNet,,mliap,https://github.com/gasteigerjo/dimenet/blob/master/LICENSE.md,https://github.com/gasteigerjo/dimenet,"DimeNet and DimeNet++ models, as proposed in Directional Message Passing for Molecular Graphs (ICLR 2020) and Fast and..",9,True,,gasteigerjo/dimenet,https://github.com/gasteigerjo/dimenet,2020-02-14 12:40:15,2023-08-01 19:36:15.000000,2023-08-01 19:36:15,102.0,2.0,52.0,5.0,,1.0,29.0,244.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+140,Allegro,,mliap,MIT,https://github.com/mir-group/allegro,Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic..,9,True,,mir-group/allegro,https://github.com/mir-group/allegro,2022-02-06 23:50:40,2023-05-08 21:16:45.000000,2023-05-08 21:16:45,38.0,,31.0,16.0,2.0,6.0,12.0,221.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+141,SchNet,,mliap,MIT,https://github.com/atomistic-machine-learning/SchNet,SchNet - a deep learning architecture for quantum chemistry.,9,False,,atomistic-machine-learning/SchNet,https://github.com/atomistic-machine-learning/SchNet,2017-10-03 11:52:20,2018-09-04 08:42:35.000000,2018-09-04 08:42:34,53.0,,57.0,16.0,,1.0,2.0,176.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+142,QDF for molecule,,ml-esm,MIT,https://github.com/masashitsubaki/QuantumDeepField_molecule,"Quantum deep field: data-driven wave function, electron density generation, and energy prediction and extrapolation..",9,False,,masashitsubaki/QuantumDeepField_molecule,https://github.com/masashitsubaki/QuantumDeepField_molecule,2020-11-11 01:06:09,2021-02-20 03:46:18.000000,2021-02-20 03:46:09,20.0,,38.0,3.0,,,3.0,167.0,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
+143,ACE.jl,,mliap,https://github.com/ACEsuit/ACE.jl/blob/main/license/mit.md,https://github.com/ACEsuit/ACE.jl,Parameterisation of Equivariant Properties of Particle Systems.,9,True,['lang-julia'],ACEsuit/ACE.jl,https://github.com/ACEsuit/ACE.jl,2019-11-30 16:22:51,2023-06-09 21:31:30.000000,2023-06-09 21:29:10,912.0,1.0,15.0,8.0,65.0,23.0,58.0,61.0,,,,12.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+144,GATGNN: Global Attention Graph Neural Network,,rep-learn,MIT,https://github.com/superlouis/GATGNN,Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials..,9,True,,superlouis/GATGNN,https://github.com/superlouis/GATGNN,2020-06-21 03:27:36,2022-10-03 21:57:33.000000,2022-10-03 21:57:33,99.0,,18.0,8.0,,3.0,3.0,60.0,2021-04-05 06:49:29,0.2,2.0,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+145,PROPhet,,ml-dft,GPL-3.0,https://github.com/biklooost/PROPhet,PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches.,9,False,"['mliap', 'md', 'single-paper', 'lang-cpp']",biklooost/PROPhet,https://github.com/biklooost/PROPhet,2016-09-16 16:21:06,2018-04-19 02:09:46.000000,2018-04-19 02:00:46,120.0,,26.0,14.0,6.0,8.0,7.0,59.0,2018-04-15 16:55:15,1.2,3.0,4.0,,,,,,,,,,,2.0,,,,,,,,,True,,,,,,,
+146,DeepErwin,,ml-wft,https://github.com/mdsunivie/deeperwin/blob/master/LICENSE,https://github.com/mdsunivie/deeperwin,DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions..,9,True,,mdsunivie/deeperwin,https://github.com/mdsunivie/deeperwin,2021-06-14 15:18:32,2023-07-17 13:48:55.000000,2023-07-17 13:39:49,55.0,4.0,5.0,3.0,3.0,1.0,7.0,33.0,2022-07-18 10:18:25,arxiv_2105.08351v2,2.0,6.0,deeperwin,,,,https://pypi.org/project/deeperwin,8.0,8.0,,,,3.0,,,,,,,,,,,,,,,,
+147,GAP,,mliap,https://github.com/libAtoms/GAP/blob/main/LICENSE.md,https://libatoms.github.io/,Gaussian Approximation Potential (GAP).,9,True,,libAtoms/GAP,https://github.com/libAtoms/GAP,2021-03-22 14:48:56,2023-06-08 10:42:38.000000,2023-06-08 10:42:38,199.0,4.0,20.0,10.0,63.0,,,30.0,,,,12.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+148,ALF,,mliap,https://github.com/lanl/ALF/blob/main/LICENSE,https://github.com/lanl/ALF,A framework for performing active learning for training machine-learned interatomic potentials.,9,True,['active-learning'],lanl/alf,https://github.com/lanl/ALF,2023-01-04 23:13:24,2023-08-23 13:36:54.000000,2023-08-04 15:53:59,139.0,61.0,7.0,6.0,23.0,,,19.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+149,CGAT,,rep-learn,MIT,https://github.com/hyllios/CGAT,Crystal graph attention neural networks for materials prediction.,9,True,,hyllios/CGAT,https://github.com/hyllios/CGAT,2021-03-28 09:51:15,2023-07-18 12:04:35.000000,2023-01-10 22:31:07,153.0,,7.0,3.0,1.0,,1.0,13.0,2023-07-18 12:04:35,0.1,1.0,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+150,SALTED,,ml-dft,GPL-3.0,https://github.com/andreagrisafi/SALTED,Symmetry-Adapted Learning of Three-dimensional Electron Densities.,9,True,,andreagrisafi/SALTED,https://github.com/andreagrisafi/SALTED,2020-01-22 10:24:29,2023-08-23 15:04:49.000000,2023-08-22 12:13:16,156.0,17.0,2.0,1.0,5.0,1.0,,12.0,2023-04-10 16:25:44,2.0.0,1.0,7.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+151,ACE1Pack.jl,,mliap,MIT,https://github.com/ACEsuit/ACE1pack.jl,"Provides convenience functionality for the usage of ACE1.jl, ACEfit.jl, JuLIP.jl for fitting interatomic potentials..",9,True,['lang-julia'],ACEsuit/ACE1pack.jl,https://github.com/ACEsuit/ACE1pack.jl,2023-08-21 16:25:00,2023-08-21 16:30:19.000000,2023-08-21 15:48:54,547.0,50.0,,,,,,,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,https://acesuit.github.io/ACE1pack.jl,,,
+152,RDKit Tutorials,,educational,https://github.com/rdkit/rdkit-tutorials/blob/master/LICENSE,https://github.com/rdkit/rdkit-tutorials,Tutorials to learn how to work with the RDKit.,8,True,,rdkit/rdkit-tutorials,https://github.com/rdkit/rdkit-tutorials,2016-10-07 03:34:01,2023-03-19 13:36:55.000000,2023-03-19 13:36:55,68.0,,65.0,16.0,7.0,3.0,1.0,199.0,,,,5.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+153,The Collection of Database and Dataset Resources in Materials Science,,community,,https://github.com/sedaoturak/data-resources-for-materials-science,"A list of databases, datasets and books/handbooks where you can find materials properties for machine learning..",8,True,['datasets'],sedaoturak/data-resources-for-materials-science,https://github.com/sedaoturak/data-resources-for-materials-science,2021-02-20 06:38:45,2023-06-27 13:32:27.000000,2023-06-27 13:32:27,26.0,3.0,25.0,9.0,1.0,,,155.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+154,GemNet,,mliap,https://github.com/TUM-DAML/gemnet_pytorch/blob/master/LICENSE,https://github.com/TUM-DAML/gemnet_pytorch,"GemNet model in PyTorch, as proposed in GemNet: Universal Directional Graph Neural Networks for Molecules (NeurIPS..",8,True,,TUM-DAML/gemnet_pytorch,https://github.com/TUM-DAML/gemnet_pytorch,2021-10-11 07:30:36,2023-04-26 14:20:12.000000,2023-04-26 14:20:12,36.0,,25.0,4.0,1.0,,13.0,147.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+155,G-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/G-SchNet,G-SchNet - a generative model for 3d molecular structures.,8,True,,atomistic-machine-learning/G-SchNet,https://github.com/atomistic-machine-learning/G-SchNet,2019-10-21 13:48:59,2023-03-24 12:05:41.000000,2023-03-24 12:05:41,64.0,,22.0,6.0,,,10.0,115.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+156,ANI-1 Dataset,,datasets,MIT,https://github.com/isayev/ANI1_dataset,A data set of 20 million calculated off-equilibrium conformations for organic molecules.,8,True,,isayev/ANI1_dataset,https://github.com/isayev/ANI1_dataset,2017-08-07 20:08:46,2022-08-08 15:56:17.000000,2022-08-08 15:56:17,25.0,,19.0,12.0,2.0,6.0,3.0,87.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+157,MoleculeNet Leaderboard,,datasets,MIT,https://github.com/deepchem/moleculenet,,8,False,['benchmarking'],deepchem/moleculenet,https://github.com/deepchem/moleculenet,2020-02-24 18:14:05,2021-04-29 19:51:06.000000,2021-04-29 19:51:06,78.0,,18.0,5.0,15.0,22.0,5.0,73.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+158,matsciml,,rep-learn,MIT,https://github.com/IntelLabs/matsciml,Open MatSci ML Toolkit is a single framework for prototyping and scaling out deep learning models for materials..,8,True,['workflows'],IntelLabs/matsciml,https://github.com/IntelLabs/matsciml,2022-09-13 20:27:28,2023-08-11 01:25:27.000000,2023-07-11 21:56:44,22.0,1.0,8.0,4.0,35.0,4.0,5.0,48.0,,,1.0,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+159,cG-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/cG-SchNet,cG-SchNet - a conditional generative neural network for 3d molecular structures.,8,True,,atomistic-machine-learning/cG-SchNet,https://github.com/atomistic-machine-learning/cG-SchNet,2021-12-02 15:35:18,2023-03-24 12:09:56.000000,2023-03-24 12:09:56,28.0,,13.0,4.0,,,3.0,43.0,2022-02-21 13:36:41,1.0,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+160,hippynn,,rep-learn,https://github.com/lanl/hippynn/blob/main/LICENSE.txt,https://github.com/lanl/hippynn,python library for atomistic machine learning.,8,True,['workflows'],lanl/hippynn,https://github.com/lanl/hippynn,2021-11-17 00:45:13,2023-08-05 17:41:55.000000,2023-08-05 17:38:53,101.0,7.0,18.0,6.0,37.0,3.0,1.0,41.0,,,2.0,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+161,Sketchmap,,unsupervised,GPL-3.0,https://github.com/lab-cosmo/sketchmap,Suite of programs to perform non-linear dimensionality reduction -- sketch-map in particular.,8,True,['lang-cpp'],lab-cosmo/sketchmap,https://github.com/lab-cosmo/sketchmap,2014-05-20 09:33:32,2023-05-24 22:56:06.000000,2023-05-24 22:47:50,64.0,,10.0,29.0,1.0,3.0,5.0,39.0,,,,8.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+162,DeeperGATGNN,,rep-learn,MIT,https://github.com/usccolumbia/deeperGATGNN,Scalable graph neural networks for materials property prediction.,8,True,,usccolumbia/deeperGATGNN,https://github.com/usccolumbia/deeperGATGNN,2021-09-29 17:31:02,2023-04-19 18:59:51.000000,2023-04-19 18:59:31,24.0,,7.0,3.0,1.0,,7.0,32.0,2022-03-08 02:14:28,1.0,1.0,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+163,SNAP,,mliap,BSD-3-Clause,https://github.com/materialsvirtuallab/snap,Repository for spectral neighbor analysis potential (SNAP) model development.,8,False,,materialsvirtuallab/snap,https://github.com/materialsvirtuallab/snap,2017-06-26 21:56:00,2020-06-30 05:20:37.000000,2020-06-30 05:20:37,38.0,,16.0,10.0,1.0,1.0,3.0,32.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+164,SIMPLE-NN v2,,mliap,GPL-3.0,https://github.com/MDIL-SNU/SIMPLE-NN_v2,,8,False,,MDIL-SNU/SIMPLE-NN_v2,https://github.com/MDIL-SNU/SIMPLE-NN_v2,2021-03-02 09:36:49,2023-08-25 06:33:45.000000,2023-08-25 06:33:45,498.0,6.0,13.0,5.0,86.0,3.0,7.0,24.0,,,,12.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+165,Atomistic Adversarial Attacks,,mliap,MIT,https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks,Code for performing adversarial attacks on atomistic systems using NN potentials.,8,True,['probabilistic'],learningmatter-mit/Atomistic-Adversarial-Attacks,https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks,2021-03-28 17:39:52,2022-10-03 16:19:31.000000,2022-10-03 16:19:29,33.0,,6.0,5.0,1.0,,1.0,24.0,2021-07-19 18:09:36,1.0.1,1.0,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+166,escnn_jax,,rep-learn,https://github.com/emilemathieu/escnn_jax/blob/master/LICENSE,https://github.com/emilemathieu/escnn_jax,Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/.,8,True,,emilemathieu/escnn_jax,https://github.com/emilemathieu/escnn_jax,2023-06-15 09:45:45,2023-06-28 14:40:32.000000,2023-06-28 14:39:56,203.0,21.0,2.0,,,,,21.0,,,,8.0,escnn_jax,,,,https://pypi.org/project/escnn_jax,,,,,,3.0,,,,,,,,,True,,,,,,,
+167,SkipAtom,,rep-eng,MIT,https://github.com/lantunes/skipatom,"Distributed representations of atoms, inspired by the Skip-gram model.",8,False,,lantunes/skipatom,https://github.com/lantunes/skipatom,2021-06-19 13:09:13,2023-07-16 19:28:39.000000,2022-05-04 13:18:30,46.0,,3.0,2.0,7.0,3.0,1.0,21.0,,,1.0,,skipatom,conda-forge/skipatom,,,https://pypi.org/project/skipatom,22.0,101.0,https://anaconda.org/conda-forge/skipatom,2023-06-18 08:42:05.505,1029.0,2.0,,,,,,,,,True,,,,,,,
+168,AdsorbML,,rep-learn,MIT,https://github.com/Open-Catalyst-Project/AdsorbML,,8,True,"['surface-science', 'single-paper']",Open-Catalyst-Project/AdsorbML,https://github.com/Open-Catalyst-Project/AdsorbML,2022-11-30 01:38:20,2023-07-31 16:28:14.000000,2023-07-31 16:28:09,56.0,16.0,4.0,6.0,10.0,,1.0,17.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+169,iam-notebooks,,educational,Apache-2.0,https://github.com/ceriottm/iam-notebooks,Jupyter notebooks for the lectures of the Introduction to Atomistic Modeling.,8,True,,ceriottm/iam-notebooks,https://github.com/ceriottm/iam-notebooks,2020-11-23 21:27:41,2023-08-07 23:02:34.000000,2023-08-07 23:02:34,228.0,1.0,4.0,4.0,7.0,3.0,,16.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+170,wfl,,mliap,,https://github.com/libAtoms/workflow,Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows.,8,True,"['workflows', 'htc']",libAtoms/workflow,https://github.com/libAtoms/workflow,2021-11-04 17:03:34,2023-08-22 11:50:25.000000,2023-08-17 18:43:05,898.0,6.0,13.0,9.0,137.0,58.0,66.0,13.0,,,,12.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,
+171,UVVisML,,rep-learn,MIT,https://github.com/learningmatter-mit/uvvisml,Predict optical properties of molecules with machine learning.,8,True,"['optical-properties', 'single-paper', 'probabilistic']",learningmatter-mit/uvvisml,https://github.com/learningmatter-mit/uvvisml,2021-10-13 05:58:48,2023-05-26 22:35:14.000000,2023-05-26 22:35:14,17.0,,4.0,4.0,1.0,,,11.0,2022-02-06 18:14:14,0.0.2,2.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+172,bVAE-IM,,generative,MIT,https://github.com/tsudalab/bVAE-IM,Implementation of Chemical Design with GPU-based Ising Machine.,8,True,"['qml', 'single-paper']",tsudalab/bVAE-IM,https://github.com/tsudalab/bVAE-IM,2023-03-01 08:26:56,2023-07-11 04:39:24.000000,2023-07-11 04:39:24,39.0,6.0,2.0,8.0,,,,8.0,2023-03-01 14:26:13,1.0.0,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+173,MAChINE,,educational,MIT,https://github.com/aimat-lab/MAChINE,Client-Server Web App to introduce usage of ML in materials science to beginners.,8,False,,aimat-lab/MAChINE,https://github.com/aimat-lab/MAChINE,2023-04-17 14:29:06,2023-08-24 14:39:40.000000,2023-08-08 11:38:34,984.0,64.0,,,4.0,15.0,16.0,1.0,,,,7.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+174,"Data Handling, DoE and Statistical Analysis for Material Chemists",,educational,GPL-3.0,https://github.com/Teoroo-CMC/DoE_Course_Material,"Notebooks for workshops of DoE course, hosted by the Computational Materials Chemistry group at Uppsala University.",8,True,,Teoroo-CMC/DoE_Course_Material,https://github.com/Teoroo-CMC/DoE_Course_Material,2023-05-22 08:11:41,2023-06-26 12:48:17.000000,2023-06-26 12:48:15,157.0,112.0,12.0,2.0,1.0,,,,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+175,Equiformer,,rep-learn,MIT,https://github.com/atomicarchitects/equiformer,[ICLR23 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs.,7,True,,atomicarchitects/equiformer,https://github.com/atomicarchitects/equiformer,2023-02-28 00:21:30,2023-06-21 08:04:30.000000,2023-06-21 08:03:53,3.0,2.0,22.0,5.0,1.0,5.0,7.0,119.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+176,A Highly Opinionated List of Open-Source Materials Informatics Resources,,community,MIT,https://github.com/ncfrey/resources,A Highly Opinionated List of Open Source Materials Informatics Resources.,7,False,,ncfrey/resources,https://github.com/ncfrey/resources,2020-11-17 23:47:07,2022-02-18 13:37:51.000000,2022-02-18 13:37:51,8.0,,19.0,9.0,,,,93.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+177,DTNN,,rep-learn,MIT,https://github.com/atomistic-machine-learning/dtnn,Deep Tensor Neural Network.,7,False,,atomistic-machine-learning/dtnn,https://github.com/atomistic-machine-learning/dtnn,2017-03-10 14:40:05,2017-07-11 08:26:15.000000,2017-07-11 08:25:39,9.0,,30.0,15.0,,,3.0,77.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+178,AIMNet,,mliap,MIT,https://github.com/aiqm/aimnet,Atoms In Molecules Neural Network Potential.,7,False,['single-paper'],aiqm/aimnet,https://github.com/aiqm/aimnet,2018-09-26 17:28:37,2019-11-21 23:49:01.000000,2019-11-21 23:49:00,7.0,,20.0,10.0,2.0,3.0,,75.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+179,JAXChem,,general-tool,MIT,https://github.com/deepchem/jaxchem,JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling.,7,False,,deepchem/jaxchem,https://github.com/deepchem/jaxchem,2020-05-11 18:54:41,2020-07-15 05:02:21.000000,2020-07-15 04:55:41,96.0,,9.0,7.0,13.0,1.0,1.0,74.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+180,PhysNet,,mliap,MIT,https://github.com/MMunibas/PhysNet,Code for training PhysNet models.,7,False,['electrostatics'],MMunibas/PhysNet,https://github.com/MMunibas/PhysNet,2019-03-28 09:05:22,2022-10-16 17:45:42.000000,2020-12-07 11:09:20,4.0,,26.0,9.0,1.0,3.0,,74.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+181,Cormorant,,rep-learn,https://github.com/risilab/cormorant/blob/master/LICENSE,https://github.com/risilab/cormorant,Codebase for Cormorant Neural Networks.,7,False,,risilab/cormorant,https://github.com/risilab/cormorant,2019-10-27 18:22:07,2022-05-11 12:49:05.000000,2020-03-11 15:25:51,160.0,,10.0,6.0,1.0,3.0,,53.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+182,uncertainty_benchmarking,,general-tool,,https://github.com/ulissigroup/uncertainty_benchmarking,Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions.,7,False,"['benchmarking', 'probabilistic']",ulissigroup/uncertainty_benchmarking,https://github.com/ulissigroup/uncertainty_benchmarking,2019-08-28 19:39:28,2021-06-07 23:29:39.000000,2021-06-07 23:27:19,265.0,,6.0,6.0,1.0,,,34.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+183,MACE-Jax,,mliap,MIT,https://github.com/ACEsuit/mace-jax,Equivariant machine learning interatomic potentials in JAX.,7,True,,ACEsuit/mace-jax,https://github.com/ACEsuit/mace-jax,2023-02-06 12:10:16,2023-07-20 18:07:51.000000,2023-07-20 18:08:42,206.0,8.0,1.0,9.0,1.0,,1.0,33.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+184,torchchem,,general-tool,MIT,https://github.com/deepchem/torchchem,An experimental repo for experimenting with PyTorch models.,7,False,,deepchem/torchchem,https://github.com/deepchem/torchchem,2020-03-07 17:06:44,2023-03-24 23:13:19.000000,2020-05-01 20:12:23,49.0,,14.0,8.0,27.0,2.0,1.0,33.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+185,BOSS,,materials-discovery,,https://gitlab.com/cest-group/boss,Bayesian Optimization Structure Search (BOSS).,7,False,['probabilistic'],,,2020-02-12 08:48:33,2020-02-12 08:48:33.000000,,,,9.0,,,4.0,21.0,18.0,,,13.0,,aalto-boss,,,,https://pypi.org/project/aalto-boss,194.0,194.0,,,,2.0,,,,,,,,,,,,,,cest-group/boss,https://gitlab.com/cest-group/boss,
+186,GElib,,math,MPL-2.0,https://github.com/risi-kondor/GElib,C++/CUDA library for SO(3) equivariant operations.,7,True,['lang-cpp'],risi-kondor/GElib,https://github.com/risi-kondor/GElib,2021-08-24 20:56:40,2023-08-02 00:27:18.000000,2023-07-23 04:10:48,562.0,20.0,2.0,2.0,2.0,3.0,1.0,16.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+187,CBFV,,rep-eng,,https://github.com/Kaaiian/CBFV,Tool to quickly create a composition-based feature vector.,7,False,,kaaiian/CBFV,https://github.com/Kaaiian/CBFV,2019-09-05 23:07:46,2022-03-30 05:47:53.000000,2021-10-24 17:10:17,49.0,,5.0,4.0,7.0,5.0,5.0,13.0,,,,3.0,CBFV,,3.0,3.0,https://pypi.org/project/CBFV,76.0,76.0,,,,3.0,,,,,,,,,,,,,,,,
+188,Libnxc,,ml-dft,MPL-2.0,https://github.com/semodi/libnxc,A library for using machine-learned exchange-correlation functionals for density-functional theory.,7,False,"['lang-cpp', 'lang-fortran']",semodi/libnxc/,https://github.com/semodi/libnxc,2020-07-01 18:21:50,2021-09-18 14:53:52.000000,2021-08-14 16:26:32,100.0,,4.0,2.0,3.0,13.0,3.0,13.0,,,2.0,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+189,BenchML,,rep-eng,Apache-2.0,https://github.com/capoe/benchml,ML benchmarking and pipeling framework.,7,True,['benchmarking'],capoe/benchml,https://github.com/capoe/benchml,2020-04-28 13:26:29,2023-05-24 15:13:06.000000,2023-05-24 15:04:57,341.0,,2.0,5.0,7.0,3.0,10.0,13.0,,,,9.0,benchml,,,,https://pypi.org/project/benchml,12.0,12.0,,,,3.0,,,,,,,,,,,,,,,,
+190,ACEhamiltonians,,ml-dft,MIT,https://github.com/ACEsuit/ACEhamiltonians.jl,"Provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-..",7,True,['lang-julia'],ACEsuit/ACEhamiltonians.jl,https://github.com/ACEsuit/ACEhamiltonians.jl,2022-01-17 20:54:22,2023-04-12 15:11:09.000000,2023-04-12 15:04:14,33.0,,4.0,4.0,41.0,1.0,3.0,7.0,2022-05-20 17:07:42,arXiv.2111.13736,1.0,4.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+191,Equisolve,,general-tool,BSD-3-Clause,https://github.com/lab-cosmo/equisolve,A package tasked with taking equistore objects and computing machine learning models using them.,7,False,['mliap'],lab-cosmo/equisolve,https://github.com/lab-cosmo/equisolve,2022-10-04 15:29:19,2023-08-09 11:37:22.000000,2023-07-10 13:27:31,37.0,5.0,2.0,14.0,37.0,17.0,4.0,4.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+192,pyLODE,,rep-eng,Apache-2.0,https://github.com/ceriottm/lode,Pythonic implementation of LOng Distance Equivariants.,7,False,['electrostatics'],ceriottm/lode,https://github.com/ceriottm/lode,2022-01-19 17:01:38,2023-07-05 09:57:29.000000,2023-07-05 09:57:14,241.0,3.0,1.0,3.0,,1.0,,2.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+193,BestPractices,,educational,MIT,https://github.com/anthony-wang/BestPractices,Things that you should (and should not) do in your Materials Informatics research.,6,False,,anthony-wang/BestPractices,https://github.com/anthony-wang/BestPractices,2020-05-05 19:41:25,2023-03-10 21:27:15.000000,2021-02-18 08:56:47,15.0,,63.0,7.0,7.0,5.0,2.0,134.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+194,EquiformerV2,,rep-learn,MIT,https://github.com/atomicarchitects/equiformer_v2,[arXiv23] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations.,6,True,,atomicarchitects/equiformer_v2,https://github.com/atomicarchitects/equiformer_v2,2023-06-21 07:09:58,2023-07-28 07:30:45.000000,2023-07-28 07:30:27,8.0,8.0,5.0,5.0,,2.0,,62.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,
+195,Applied AI for Materials,,educational,,https://github.com/WardLT/applied-ai-for-materials,Course materials for Applied AI for Materials Science and Engineering.,6,False,,WardLT/applied-ai-for-materials,https://github.com/WardLT/applied-ai-for-materials,2020-10-12 19:39:06,2022-03-12 02:26:58.000000,2022-03-12 02:26:41,107.0,,28.0,4.0,13.0,5.0,,46.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+196,ANI-1x Datasets,,datasets,MIT,https://github.com/aiqm/ANI1x_datasets,"The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules.",6,False,,aiqm/ANI1x_datasets,https://github.com/aiqm/ANI1x_datasets,2019-09-17 18:19:28,2022-04-11 17:25:55.000000,2022-04-11 17:25:55,12.0,,5.0,6.0,,2.0,3.0,45.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+197,COMP6 Benchmark dataset,,datasets,MIT,https://github.com/isayev/COMP6,COMP6 Benchmark dataset for ML potentials.,6,False,,isayev/COMP6,https://github.com/isayev/COMP6,2017-12-29 16:58:35,2018-07-09 23:56:35.000000,2018-07-09 23:56:34,27.0,,4.0,5.0,,2.0,1.0,35.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+198,DeepDFT,,ml-dft,MIT,https://github.com/peterbjorgensen/DeepDFT,Official implementation of DeepDFT model.,6,True,,peterbjorgensen/DeepDFT,https://github.com/peterbjorgensen/DeepDFT,2020-11-03 11:51:15,2023-02-28 15:37:49.000000,2023-02-28 15:37:37,128.0,,6.0,1.0,,,3.0,35.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+199,milad,,rep-eng,GPL-3.0,https://github.com/muhrin/milad,Moment Invariants Local Atomic Descriptor.,6,True,['generative'],muhrin/milad,https://github.com/muhrin/milad,2020-04-23 09:14:24,2022-12-03 10:40:05.000000,2022-12-03 10:39:59,110.0,,1.0,4.0,,,,27.0,,,,,,,1.0,1.0,,,,,,,3.0,,,,,,,,,,,,,,,,
+200,MACE-Layer,,rep-learn,MIT,https://github.com/ACEsuit/mace-layer,Higher order equivariant graph neural networks for 3D point clouds.,6,True,,ACEsuit/mace-layer,https://github.com/ACEsuit/mace-layer,2022-11-09 17:03:41,2023-06-27 15:32:49.000000,2023-06-06 10:09:58,19.0,1.0,3.0,6.0,2.0,1.0,,25.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+201,PACE,,md,https://github.com/ICAMS/lammps-user-pace/blob/main/LICENSE,https://github.com/ICAMS/lammps-user-pace,"The LAMMPS MLIAP `pair_style pace`, aka Atomic Cluster Expansion (ACE), aka ML-PACE,..",6,True,,ICAMS/lammps-user-pace,https://github.com/ICAMS/lammps-user-pace,2021-02-25 10:04:48,2023-05-25 22:16:12.000000,2023-01-31 19:53:46,46.0,,10.0,5.0,11.0,1.0,5.0,20.0,,,1.0,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+202,GLAMOUR,,rep-learn,MIT,https://github.com/learningmatter-mit/GLAMOUR,Graph Learning over Macromolecule Representations.,6,True,['single-paper'],learningmatter-mit/GLAMOUR,https://github.com/learningmatter-mit/GLAMOUR,2021-08-20 18:16:40,2022-12-31 17:56:21.000000,2022-12-31 17:56:21,14.0,,5.0,3.0,,,8.0,18.0,2021-08-23 18:58:52,0.1,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+203,SA-GPR,,rep-eng,LGPL-3.0,https://github.com/dilkins/TENSOAP,Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR).,6,True,['lang-c'],dilkins/TENSOAP,https://github.com/dilkins/TENSOAP,2020-05-04 14:19:01,2023-04-07 09:58:08.000000,2022-09-29 09:30:45,25.0,,9.0,3.0,10.0,2.0,5.0,14.0,2020-12-17 16:51:47,2020.0,1.0,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+204,testing-framework,,mliap,,https://github.com/libAtoms/testing-framework,The purpose of this repository is to aid the testing of a large number of interatomic potentials for a variety of..,6,False,['benchmarking'],libAtoms/testing-framework,https://github.com/libAtoms/testing-framework,2020-03-04 11:43:15,2022-02-10 17:23:46.000000,2022-02-10 17:23:46,225.0,,6.0,16.0,10.0,5.0,3.0,11.0,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+205,NICE,,rep-eng,MIT,https://github.com/lab-cosmo/nice,NICE (N-body Iteratively Contracted Equivariants) is a set of tools designed for the calculation of invariant and..,6,True,,lab-cosmo/nice,https://github.com/lab-cosmo/nice,2020-07-03 08:47:41,2023-05-01 09:22:21.000000,2023-05-01 09:21:56,231.0,,2.0,6.0,7.0,2.0,1.0,10.0,,,1.0,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+206,CatGym,,reinforcement-learning,GPL,https://github.com/ulissigroup/catgym,Surface segregation using Deep Reinforcement Learning.,6,False,,ulissigroup/catgym,https://github.com/ulissigroup/catgym,2019-08-06 19:25:27,2021-08-30 17:05:36.000000,2021-08-30 17:05:32,162.0,,2.0,4.0,,1.0,,10.0,,,,7.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+207,fplib,,rep-eng,MIT,https://github.com/zhuligs/fplib,a fingerprint library.,6,False,"['lang-c', 'single-paper']",zhuligs/fplib,https://github.com/zhuligs/fplib,2015-09-07 08:18:27,2022-02-09 05:31:21.000000,2022-02-09 05:31:12,37.0,,2.0,3.0,,,3.0,7.0,2021-02-03 21:40:23,pub,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+208,SOAPxx,,rep-eng,GPL-2.0,https://github.com/capoe/soapxx,A SOAP implementation.,6,False,['lang-cpp'],capoe/soapxx,https://github.com/capoe/soapxx,2016-03-29 10:00:00,2020-03-27 13:47:44.000000,2020-03-27 13:47:36,289.0,,3.0,3.0,1.0,,2.0,7.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+209,COSMO Toolbox,,math,,https://github.com/lab-cosmo/toolbox,Assorted libraries and utilities for atomistic simulation analysis.,6,True,['lang-cpp'],lab-cosmo/toolbox,https://github.com/lab-cosmo/toolbox,2014-05-20 11:23:13,2023-06-23 15:07:59.000000,2023-06-23 15:07:29,106.0,1.0,5.0,26.0,1.0,,,6.0,,,,9.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+210,PANNA,,mliap,MIT,https://gitlab.com/PANNAdevs/panna,A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic..,6,False,['benchmarking'],,,2018-11-09 10:47:48,2018-11-09 10:47:48.000000,,,,10.0,,,,,6.0,,,2.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,PANNAdevs/panna,https://gitlab.com/PANNAdevs/panna,
+211,COSMO Software Cookbook,,educational,BSD-3-Clause,https://github.com/lab-cosmo/software-cookbook,The COSMO cookbook contains recipes for atomic-scale modelling for materials and molecules.,6,False,,lab-cosmo/software-cookbook,https://github.com/lab-cosmo/software-cookbook,2023-05-23 10:33:47,2023-08-15 06:43:12.000000,2023-07-12 12:10:24,23.0,21.0,1.0,13.0,22.0,3.0,,2.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+212,Computational Autonomy for Materials Discovery (CAMD),,materials-discovery,Apache-2.0,https://github.com/ulissigroup/CAMD,Agent-based sequential learning software for materials discovery.,6,False,,ulissigroup/CAMD,https://github.com/ulissigroup/CAMD,2023-01-10 19:42:57,2023-01-10 19:49:35.000000,2023-01-10 19:49:13,1336.0,,,1.0,,,,1.0,,,,17.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+213,MEGAN: Multi Explanation Graph Attention Student,,xai,MIT,https://github.com/aimat-lab/graph_attention_student,Minimal implementation of graph attention student model architecture.,6,False,,aimat-lab/graph_attention_student,https://github.com/aimat-lab/graph_attention_student,2022-07-28 06:22:50,2023-08-08 14:03:12.000000,2023-08-08 14:03:07,21.0,9.0,1.0,3.0,1.0,,,1.0,,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
+214,MEGAN,,xai,MIT,https://github.com/aimat-lab/graph_attention_student,Minimal implementation of graph attention student model architecture.,6,False,"['xai', 'rep-learn']",aimat-lab/graph_attention_student,https://github.com/aimat-lab/graph_attention_student,2022-07-28 06:22:50,2023-08-08 14:03:12.000000,2023-08-08 14:03:07,21.0,9.0,1.0,3.0,1.0,,,1.0,,,,,,,,,,,,,,,2.0,,,,,,,,,True,,,,,,,
+215,tensorfieldnetworks,,rep-learn,MIT,https://github.com/tensorfieldnetworks/tensorfieldnetworks,,5,False,,tensorfieldnetworks/tensorfieldnetworks,https://github.com/tensorfieldnetworks/tensorfieldnetworks,2018-02-09 23:18:13,2020-01-07 17:22:16.000000,2020-01-07 17:22:15,10.0,,28.0,9.0,2.0,,2.0,144.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,
+216,GEOM,,datasets,,https://github.com/learningmatter-mit/geom,GEOM: Energy-annotated molecular conformations.,5,False,['drug-discovery'],learningmatter-mit/geom,https://github.com/learningmatter-mit/geom,2020-06-03 17:58:37,2022-04-24 18:57:39.000000,2022-04-24 18:57:39,95.0,,17.0,9.0,,1.0,9.0,131.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+217,AI4Science101,,educational,,https://github.com/deepmodeling/AI4Science101,AI for Science.,5,True,,deepmodeling/AI4Science101,https://github.com/deepmodeling/AI4Science101,2022-06-19 02:26:48,2022-09-04 02:06:18.000000,2022-09-04 02:06:18,139.0,,11.0,10.0,28.0,,1.0,69.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+218,SchNOrb,,ml-wft,MIT,https://github.com/atomistic-machine-learning/SchNOrb,Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions.,5,False,,atomistic-machine-learning/SchNOrb,https://github.com/atomistic-machine-learning/SchNOrb,2019-09-17 12:41:48,2019-09-17 14:31:47.000000,2019-09-17 14:31:19,2.0,,17.0,5.0,,1.0,,53.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+219,Machine Learning for Materials Hard and Soft,,educational,,https://github.com/CompPhysVienna/MLSummerSchoolVienna2022,ESI-DCAFM-TACO-VDSP Summer School on Machine Learning for Materials Hard and Soft.,5,False,,CompPhysVienna/MLSummerSchoolVienna2022,https://github.com/CompPhysVienna/MLSummerSchoolVienna2022,2022-07-01 08:42:41,2022-07-22 08:10:24.000000,2022-07-22 08:10:24,49.0,,16.0,1.0,14.0,,,31.0,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+220,DeepH-E3,,ml-dft,MIT,https://github.com/Xiaoxun-Gong/DeepH-E3,General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian.,5,True,['magnetism'],Xiaoxun-Gong/DeepH-E3,https://github.com/Xiaoxun-Gong/DeepH-E3,2023-03-16 11:25:58,2023-04-04 13:27:01.000000,2023-04-04 13:26:27,16.0,,8.0,4.0,,2.0,5.0,29.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+221,Autobahn,,rep-learn,MIT,https://github.com/risilab/Autobahn,Repository for Autobahn: Automorphism Based Graph Neural Networks.,5,False,,risilab/Autobahn,https://github.com/risilab/Autobahn,2021-03-02 01:14:40,2022-03-01 21:04:09.000000,2022-03-01 21:04:04,11.0,,2.0,5.0,,,,26.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+222,charge_transfer_nnp,,rep-learn,MIT,https://github.com/pfnet-research/charge_transfer_nnp,Graph neural network potential with charge transfer.,5,False,['electrostatics'],pfnet-research/charge_transfer_nnp,https://github.com/pfnet-research/charge_transfer_nnp,2022-04-06 01:48:18,2022-04-06 01:53:35.000000,2022-04-06 01:53:22,1.0,,4.0,12.0,,,,20.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+223,EquivariantOperators.jl,,math,MIT,https://github.com/aced-differentiate/EquivariantOperators.jl,,5,False,['lang-julia'],aced-differentiate/EquivariantOperators.jl,https://github.com/aced-differentiate/EquivariantOperators.jl,2021-11-29 03:36:21,2023-01-20 21:05:50.000000,2023-01-20 21:05:49,58.0,,,4.0,,,,17.0,,,,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,
+224,SCFNN,,rep-learn,MIT,https://github.com/andy90/SCFNN,Self-consistent determination of long-range electrostatics in neural network potentials.,5,False,"['lang-cpp', 'electrostatics', 'single-paper']",andy90/SCFNN,https://github.com/andy90/SCFNN,2021-09-22 12:02:00,2022-01-30 02:29:03.000000,2022-01-24 09:40:40,10.0,,8.0,2.0,,,,14.0,2022-01-30 02:29:04,1.0.0,1.0,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+225,rxngenerator,,generative,MIT,https://github.com/tsudalab/rxngenerator,A generative model for molecular generation via multi-step chemical reactions.,5,True,,tsudalab/rxngenerator,https://github.com/tsudalab/rxngenerator,2021-06-18 07:44:53,2022-08-09 07:21:44.000000,2022-08-09 07:21:05,16.0,,2.0,9.0,2.0,1.0,,11.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+226,AGOX,,materials-discovery,GPL-3.0,https://gitlab.com/agox/agox,AGOX is a package for global optimization of atomic system using e.g. the energy calculated from density functional..,5,False,['structure-optimization'],,,2022-03-08 09:08:13,2022-03-08 09:08:13.000000,,,,3.0,,,8.0,5.0,10.0,,,2.0,,agox,,,,https://pypi.org/project/agox,15.0,15.0,,,,2.0,,,,,,,,,,,,,,agox/agox,https://gitlab.com/agox/agox,
+227,FieldSchNet,,rep-learn,MIT,https://github.com/atomistic-machine-learning/field_schnet,,5,False,,atomistic-machine-learning/field_schnet,https://github.com/atomistic-machine-learning/field_schnet,2020-11-18 10:26:59,2022-05-19 09:28:38.000000,2022-05-19 09:28:38,26.0,,3.0,3.0,1.0,1.0,,9.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+228,3DSC Database,,datasets,https://github.com/aimat-lab/3DSC/blob/main/LICENSE.md,https://github.com/aimat-lab/3DSC,Repo for the paper publishing the superconductor database with 3D crystal structures.,5,True,"['superconductors', 'materials-discovery']",aimat-lab/3DSC,https://github.com/aimat-lab/3DSC,2021-11-02 09:07:57,2023-07-21 09:28:43.000000,2023-07-21 09:26:12,52.0,6.0,2.0,2.0,,,,6.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+229,SciGlass,,datasets,MIT,https://github.com/drcassar/SciGlass,The database contains a vast set of data on the properties of glass materials.,5,True,,drcassar/SciGlass,https://github.com/drcassar/SciGlass,2019-06-19 19:36:32,2023-03-10 18:05:17.000000,2023-03-10 18:05:06,27.0,,3.0,1.0,,,,6.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,
+230,CraTENet,,rep-learn,MIT,https://github.com/lantunes/CraTENet,An attention-based deep neural network for thermoelectric transport properties.,5,True,['transport-phenomena'],lantunes/CraTENet,https://github.com/lantunes/CraTENet,2022-06-30 10:40:06,2023-04-05 01:13:22.000000,2023-04-05 01:13:11,24.0,,1.0,1.0,,,,6.0,,,,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,
+231,Alchemical learning,,mliap,BSD-3-Clause,https://github.com/Luthaf/alchemical-learning,Code for the Modeling high-entropy transition metal alloys with alchemical compression article.,5,False,,Luthaf/alchemical-learning,https://github.com/Luthaf/alchemical-learning,2021-12-02 17:02:00,2023-04-24 18:35:45.000000,2023-04-07 10:19:10,120.0,,1.0,6.0,1.0,,4.0,2.0,,,,10.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+232,BERT-PSIE-TC,,lm,MIT,https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC,A dataset of Curie temperatures automatically extracted from scientific literature with the use of the BERT-PSIE..,5,False,['magnetism'],StefanoSanvitoGroup/BERT-PSIE-TC,https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC,2023-01-25 10:27:26,2023-08-18 11:47:45.000000,2023-08-18 12:48:31,36.0,13.0,2.0,1.0,,,,2.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+233,linear-regression-benchmarks,,datasets,MIT,https://github.com/BingqingCheng/linear-regression-benchmarks,Data sets used for linear regression benchmarks.,5,False,"['benchmarking', 'single-paper']",BingqingCheng/linear-regression-benchmarks,https://github.com/BingqingCheng/linear-regression-benchmarks,2020-04-16 20:48:28,2022-01-26 08:29:46.000000,2022-01-26 08:29:46,24.0,,,3.0,2.0,,,1.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+234,COSMO tools,,others,,https://github.com/lab-cosmo/cosmo-tools,"Scripts, jupyter nbs, and general helpful stuff from COSMO by COSMO.",5,False,,lab-cosmo/cosmo-tools,https://github.com/lab-cosmo/cosmo-tools,2018-11-06 09:40:00,2022-11-25 11:19:48.000000,2022-11-25 11:19:29,59.0,,3.0,22.0,,,,1.0,,,,4.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,True
+235,Per-Site CGCNN,,rep-learn,MIT,https://github.com/learningmatter-mit/per-site_cgcnn,Crystal graph convolutional neural networks for predicting material properties.,5,False,"['pre-trained', 'single-paper']",learningmatter-mit/per-site_cgcnn,https://github.com/learningmatter-mit/per-site_cgcnn,2023-05-30 18:59:03,2023-06-05 17:38:46.000000,2023-06-05 17:38:41,28.0,3.0,,,,,,1.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+236,Visual Graph Datasets,,datasets,MIT,https://github.com/aimat-lab/visual_graph_datasets,Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations..,5,True,,aimat-lab/visual_graph_datasets,https://github.com/aimat-lab/visual_graph_datasets,2023-06-01 11:33:18,2023-06-12 15:19:12.000000,2023-06-12 15:19:08,4.0,4.0,1.0,3.0,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+237,Per-site PAiNN,,rep-learn,MIT,https://github.com/learningmatter-mit/per-site_painn,Fork of PaiNN for PerovskiteOrderingGCNNs.,5,True,"['probabilistic', 'pre-trained', 'single-paper']",learningmatter-mit/per-site_painn,https://github.com/learningmatter-mit/per-site_painn,2023-06-04 14:23:49,2023-06-05 17:35:19.000000,2023-06-05 17:30:34,123.0,1.0,,,,,,,,,,10.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+238,closed-loop-acceleration-benchmarks,,materials-discovery,MIT,https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks,Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational..,5,True,"['materials-discovery', 'active-learning', 'single-paper']",aced-differentiate/closed-loop-acceleration-benchmarks,https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks,2022-11-10 20:22:30,2023-07-25 21:25:42.000000,2023-05-02 17:07:48,17.0,,1.0,4.0,3.0,,,,,,,2.0,,,,,,,,,,,2.0,,,,,,,,,True,,,,,,,
+239,Closed-loop acceleration benchmarks,,materials-discovery,MIT,https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks,Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational..,5,True,"['materials-discovery', 'active-learning', 'single-paper']",aced-differentiate/closed-loop-acceleration-benchmarks,https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks,2022-11-10 20:22:30,2023-07-25 21:25:42.000000,2023-05-02 17:07:48,17.0,,1.0,4.0,3.0,,,,,,,2.0,,,,,,,,,,,2.0,,,,,,,,,True,,,,,,,
+240,ML-for-CurieTemp-Predictions,,rep-eng,MIT,https://github.com/msg-byu/ML-for-CurieTemp-Predictions,Machine Learning Predictions of High-Curie-Temperature Materials.,5,True,"['single-paper', 'magnetism']",msg-byu/ML-for-CurieTemp-Predictions,https://github.com/msg-byu/ML-for-CurieTemp-Predictions,2023-06-05 22:46:47,2023-06-14 19:05:50.000000,2023-06-14 19:05:47,25.0,25.0,,1.0,,,,,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,
+241,ML-in-chemistry-101,,educational,,https://github.com/BingqingCheng/ML-in-chemistry-101,The course materials for Machine Learning in Chemistry 101.,4,False,,BingqingCheng/ML-in-chemistry-101,https://github.com/BingqingCheng/ML-in-chemistry-101,2020-02-09 17:47:07,2020-10-19 08:10:31.000000,2020-10-19 08:10:30,13.0,,14.0,2.0,,,,54.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+242,Graph Transport Network,,rep-learn,https://github.com/gasteigerjo/gtn/blob/main/LICENSE.md,https://github.com/gasteigerjo/gtn,"Graph transport network (GTN), as proposed in Scalable Optimal Transport in High Dimensions for Graph Distances,..",4,False,['transport-phenomena'],gasteigerjo/gtn,https://github.com/gasteigerjo/gtn,2021-07-11 23:36:22,2023-04-26 14:22:00.000000,2023-04-26 14:22:00,9.0,,3.0,2.0,,,,14.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+243,ACEHAL,,active-learning,,https://github.com/ACEsuit/ACEHAL,Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials.,4,False,['lang-julia'],ACEsuit/ACEHAL,https://github.com/ACEsuit/ACEHAL,2023-02-24 17:33:47,2023-05-09 09:37:27.000000,2023-05-09 09:37:26,107.0,,2.0,6.0,13.0,3.0,5.0,7.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+244,chemrev-gpr,,educational,,https://github.com/gabor1/chemrev-gpr,Notebooks accompanying the paper on GPR in materials and molecules in Chemical Reviews 2020.,4,False,,gabor1/chemrev-gpr,https://github.com/gabor1/chemrev-gpr,2020-12-18 23:48:06,2021-05-04 19:21:34.000000,2021-05-04 19:21:30,10.0,,4.0,4.0,,,,5.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+245,TensorPotential,,mliap,https://github.com/ICAMS/TensorPotential/blob/main/LICENSE.md,https://cortner.github.io/ACEweb/software/,"Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic..",4,False,,ICAMS/TensorPotential,https://github.com/ICAMS/TensorPotential,2021-12-08 12:10:04,2023-07-10 16:37:18.000000,2023-07-10 16:37:18,18.0,1.0,4.0,2.0,2.0,,,5.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+246,MolSLEPA,,generative,MIT,https://github.com/tsudalab/MolSLEPA,Interpretable Fragment-based Molecule Design with Self-learning Entropic Population Annealing.,4,False,['xai'],tsudalab/MolSLEPA,https://github.com/tsudalab/MolSLEPA,2023-04-10 15:04:55,2023-04-13 12:48:49.000000,2023-04-13 12:48:49,11.0,,,8.0,2.0,,,3.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+247,ACEatoms,,general-tool,https://github.com/ACEsuit/ACEatoms.jl/blob/main/ASL.md,https://github.com/ACEsuit/ACEatoms.jl,Generic code for modelling atomic properties using ACE.,4,False,['lang-julia'],ACEsuit/ACEatoms.jl,https://github.com/ACEsuit/ACEatoms.jl,2021-03-23 23:50:03,2023-01-13 21:35:06.000000,2023-01-13 21:28:08,134.0,,1.0,3.0,14.0,4.0,3.0,2.0,,,,10.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+248,charge-density-models,,ml-dft,MIT,https://github.com/ulissigroup/charge-density-models,Tools to build charge density models using ocpmodels.,4,False,,ulissigroup/charge-density-models,https://github.com/ulissigroup/charge-density-models,2022-06-22 13:47:53,2023-07-28 17:17:13.000000,2023-05-18 18:37:07,94.0,,1.0,2.0,14.0,,1.0,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+249,magnetism-prediction,,rep-eng,Apache-2.0,https://github.com/dppant/magnetism-prediction,DFT-aided Machine Learning Search for Magnetism in Fe-based Bimetallic Chalcogenides.,4,False,"['magnetism', 'single-paper']",dppant/magnetism-prediction,https://github.com/dppant/magnetism-prediction,2022-09-13 03:58:10,2023-07-19 13:25:49.000000,2023-07-19 13:25:49,46.0,2.0,,3.0,,,,1.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+250,cnine,,math,,https://github.com/risi-kondor/cnine,Cnine tensor library.,4,False,['lang-cpp'],risi-kondor/cnine,https://github.com/risi-kondor/cnine,2022-10-07 20:54:54,2023-08-24 05:06:32.000000,2023-08-24 05:06:32,187.0,33.0,1.0,1.0,1.0,,,1.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,https://risi-kondor.github.io/cnine/,,,
+251,Wigner Kernels,,math,,https://github.com/lab-cosmo/wigner_kernels,Collection of programs to benchmark Wigner kernels.,4,False,['benchmarking'],lab-cosmo/wigner_kernels,https://github.com/lab-cosmo/wigner_kernels,2022-12-08 12:28:26,2023-07-08 15:48:41.000000,2023-07-08 15:48:37,109.0,4.0,,1.0,,,,,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+252,gprep,,ml-dft,MIT,https://gitlab.com/jmargraf/gprep,Fitting DFTB repulsive potentials with GPR.,4,False,['single-paper'],,,2019-09-30 09:15:04,2019-09-30 09:15:04.000000,,,,0.0,,,,,,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,jmargraf/gprep,https://gitlab.com/jmargraf/gprep,
+253,CSNN,,ml-dft,BSD-3-Clause,https://github.com/foxjas/CSNN,Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning.,4,False,,foxjas/CSNN,https://github.com/foxjas/CSNN,2022-05-19 15:40:49,2022-10-11 04:27:40.000000,2022-10-11 04:27:40,6.0,,,1.0,,,,,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+254,Linear vs blackbox,,xai,MIT,https://github.com/CitrineInformatics-ERD-public/linear-vs-blackbox,Code and data related to the publication: Interpretable models for extrapolation in scientific machine learning.,4,False,"['xai', 'single-paper', 'rep-eng']",CitrineInformatics-ERD-public/linear-vs-blackbox,https://github.com/CitrineInformatics-ERD-public/linear-vs-blackbox,2022-12-02 20:32:53,2022-12-16 18:48:12.000000,2022-12-16 18:48:12,4.0,,,1.0,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,
+255,Atom2Vec,,rep-learn,,https://github.com/idocx/Atom2Vec,Atom2Vec: a simple way to describe atoms for machine learning.,3,False,,idocx/Atom2Vec,https://github.com/idocx/Atom2Vec,2020-01-18 23:31:47,2022-10-16 05:43:31.000000,2021-06-21 16:31:09,3.0,,8.0,1.0,1.0,1.0,1.0,24.0,,,,,atom2vec,,1.0,1.0,https://pypi.org/project/atom2vec,15.0,15.0,,,,3.0,,,,,,,,,True,,,,,,,
+256,Coarse-Graining-Auto-encoders,,unsupervised,,https://github.com/learningmatter-mit/Coarse-Graining-Auto-encoders,,3,False,['single-paper'],learningmatter-mit/Coarse-Graining-Auto-encoders,https://github.com/learningmatter-mit/Coarse-Graining-Auto-encoders,2019-09-16 15:27:57,2019-08-16 21:39:34.000000,2019-08-16 21:39:33,14.0,,7.0,6.0,,,,19.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+257,ML-DFT,,ml-dft,MIT,https://github.com/MihailBogojeski/ml-dft,A package for density functional approximation using machine learning.,3,False,,MihailBogojeski/ml-dft,https://github.com/MihailBogojeski/ml-dft,2020-09-14 22:15:56,2020-09-18 16:36:30.000000,2020-09-18 16:36:30,9.0,,6.0,2.0,,1.0,1.0,18.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+258,CSPML (crystal structure prediction with machine learning-based element substitution),,materials-discovery,,https://github.com/Minoru938/CSPML,Original implementation of CSPML.,3,False,['structure-prediction'],minoru938/cspml,https://github.com/Minoru938/CSPML,2022-01-15 10:59:27,2022-06-02 23:26:26.000000,2022-06-02 23:26:26,7.0,,7.0,2.0,,1.0,,12.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+259,glp,,mliap,MIT,https://github.com/sirmarcel/glp,tools for graph-based machine-learning potentials in jax.,3,False,,sirmarcel/glp,https://github.com/sirmarcel/glp,2023-03-27 15:19:40,2023-08-07 14:41:10.000000,2023-08-07 14:41:05,10.0,2.0,,1.0,2.0,,,10.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+260,SPINNER,,materials-discovery,GPL-3.0,https://github.com/MDIL-SNU/SPINNER,SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random..,3,False,"['lang-cpp', 'structure-prediction']",MDIL-SNU/SPINNER,https://github.com/MDIL-SNU/SPINNER,2021-07-15 02:10:58,2021-11-25 07:58:15.000000,2021-11-25 07:58:15,102.0,,2.0,1.0,,1.0,,9.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+261,sl_discovery,,materials-discovery,Apache-2.0,https://github.com/CitrineInformatics-ERD-public/sl_discovery,Data processing and models related to Quantifying the performance of machine learning models in materials discovery.,3,False,"['materials-discovery', 'single-paper']",CitrineInformatics-ERD-public/sl_discovery,https://github.com/CitrineInformatics-ERD-public/sl_discovery,2022-10-24 18:10:14,2022-12-20 23:46:05.000000,2022-12-20 23:45:57,5.0,,2.0,2.0,,,,5.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,
+262,Element encoder,,rep-learn,GPL-3.0,https://github.com/jeherr/element-encoder,Autoencoder neural network to compress properties of atomic species into a vector representation.,3,False,['single-paper'],jeherr/element-encoder,https://github.com/jeherr/element-encoder,2019-03-27 17:11:30,2020-01-09 15:54:27.000000,2020-01-09 15:54:26,8.0,,1.0,4.0,,,1.0,5.0,,,,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,
+263,e3psi,,ml-esm,LGPL-3.0,https://github.com/muhrin/e3psi,Equivariant machine learning library for learning from electronic structures.,3,False,,muhrin/e3psi,https://github.com/muhrin/e3psi,2022-08-08 10:48:30,2023-08-09 17:04:49.000000,2023-04-10 17:04:33,14.0,,,2.0,,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+264,atom_by_atom,,rep-learn,,https://github.com/learningmatter-mit/atom_by_atom,Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with Machine Learning.,3,False,"['surface-science', 'single-paper']",learningmatter-mit/atom_by_atom,https://github.com/learningmatter-mit/atom_by_atom,2023-05-30 20:18:00,2023-06-28 20:39:26.000000,2023-06-28 20:39:13,64.0,64.0,,2.0,,,,2.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+265,DeepCDP,,ml-dft,,https://github.com/siddarthachar/deepcdp,DeepCDP: Deep learning Charge Density Prediction.,3,False,,siddarthachar/deepcdp,https://github.com/siddarthachar/deepcdp,2021-12-18 14:26:56,2023-06-16 20:38:23.000000,2023-06-16 20:38:23,96.0,5.0,,2.0,27.0,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,
+266,SISSO++,,rep-eng,Apache-2.0,https://gitlab.com/sissopp_developers/sissopp,C++ Implementation of SISSO with python bindings.,3,False,['lang-cpp'],,,2021-04-30 14:20:59,2021-04-30 14:20:59.000000,,,,2.0,,,1.0,11.0,1.0,,,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,sissopp_developers/sissopp,https://gitlab.com/sissopp_developers/sissopp,
+267,Magpie,,general-tool,MIT,https://bitbucket.org/wolverton/magpie/,Materials Agnostic Platform for Informatics and Exploration (Magpie).,3,False,['lang-java'],,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+268,MALADA,,ml-dft,BSD-3-Clause,https://github.com/mala-project/malada,MALA Data Acquisition: Helpful tools to build data for MALA.,3,False,,mala-project/malada,https://github.com/mala-project/malada,2021-07-26 05:46:08,2023-05-24 09:18:24.000000,2023-05-24 09:18:24,111.0,,1.0,2.0,4.0,17.0,2.0,,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+269,MLatom,,general-tool,https://creativecommons.org/licenses/by-nc-nd/4.0/,http://mlatom.com/,Machine learning for atomistic simulations.,3,False,,,,,,,,,,,,,,,,,,,MLatom,,,,https://pypi.org/project/MLatom,105.0,105.0,,,,3.0,,,,,,,,,,,,,http://mlatom.com/manual/,,,
+270,xDeepH,,ml-dft,LGPL-3.0,https://github.com/mzjb/xDeepH,Extended DeepH (xDeepH) method for magnetic materials.,2,False,"['magnetism', 'lang-julia']",mzjb/xDeepH,https://github.com/mzjb/xDeepH,2023-02-23 12:56:49,2023-06-14 11:44:53.000000,2023-06-14 11:44:46,4.0,1.0,,1.0,,,,16.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+271,MLIP-3,,mliap,BSD-2-Clause,https://gitlab.com/ashapeev/mlip-3,MLIP-3: Active learning on atomic environments with Moment Tensor Potentials (MTP).,2,False,['lang-cpp'],,,2023-04-24 14:05:53,2023-04-24 14:05:53.000000,,,,1.0,,,10.0,1.0,11.0,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,ashapeev/mlip-3,https://gitlab.com/ashapeev/mlip-3,
+272,SingleNN,,mliap,,https://github.com/lmj1029123/SingleNN,An efficient package for training and executing neural-network interatomic potentials.,2,False,['lang-cpp'],lmj1029123/SingleNN,https://github.com/lmj1029123/SingleNN,2020-03-11 18:36:16,2021-11-09 00:40:18.000000,2021-11-09 00:40:10,17.0,,1.0,1.0,,1.0,,6.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+273,interface-lammps-mlip-3,,md,GPL-2.0,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,An interface between LAMMPS and MLIP (version 3).,2,False,,,,2023-04-24 12:48:51,2023-04-24 12:48:51.000000,,,,2.0,,,,,5.0,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,ivannovikov/interface-lammps-mlip-3,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,
+274,MLDensity_tutorial,,educational,,https://github.com/bfocassio/MLDensity_tutorial,Tutorial files to work with ML for the charge density in molecules and solids.,2,False,,bfocassio/MLDensity_tutorial,https://github.com/bfocassio/MLDensity_tutorial,2023-01-31 10:33:23,2023-02-22 19:20:32.000000,2023-02-22 19:20:32,8.0,,1.0,1.0,,,,4.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+275,quantum-structure-ml,,general-tool,,https://github.com/hgheiberger/quantum-structure-ml,Multi-class classification model for predicting the magnetic order of magnetic structures and a binary classification..,2,False,"['magnetism', 'benchmarking']",hgheiberger/quantum-structure-ml,https://github.com/hgheiberger/quantum-structure-ml,2020-10-05 01:11:01,2022-12-22 21:45:40.000000,2022-12-22 21:45:40,19.0,,,2.0,,,,1.0,2022-08-18 05:25:24,1.0.0,1.0,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+276,ACE Workflows,,mliap,,https://github.com/ACEsuit/ACEworkflows,Workflow Examples for ACE Models.,2,False,"['lang-julia', 'workflows']",ACEsuit/ACEworkflows,https://github.com/ACEsuit/ACEworkflows,2023-04-04 16:57:36,2023-08-21 23:24:43.000000,2023-08-21 23:24:40,19.0,4.0,,3.0,6.0,1.0,,,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+277,AMP,,rep-eng,,https://bitbucket.org/andrewpeterson/amp/,Amp is an open-source package designed to easily bring machine-learning to atomistic calculations.,2,False,,,,,,,,,,,,,,,,,,,amp-atomistics,,,,https://pypi.org/project/amp-atomistics,43.0,43.0,,,,3.0,,,,,,,,,,,,,https://amp.readthedocs.io/,,,
+278,q-pac,,ml-esm,MIT,,,2,False,['electrostatics'],,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,jmargraf/kqeq,,
+279,RuNNer,,mliap,GPL-3.0,https://www.uni-goettingen.de/de/560580.html,The RuNNer Neural Network Energy Representation is a Fortran-based framework for the construction of Behler-..,2,False,['lang-fortran'],,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,https://theochemgoettingen.gitlab.io/RuNNer/,,,
+280,Point Edge Transformer,,rep-learn,CC-BY-4.0,https://zenodo.org/record/7967079,"Smooth, exact rotational symmetrization for deep learning on point clouds.",2,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+281,PiNN Lab,,educational,GPL-3.0,https://github.com/Teoroo-CMC/PiNN_lab,,1,False,,Teoroo-CMC/PiNN_lab,https://github.com/Teoroo-CMC/PiNN_lab,2019-03-17 22:09:30,2023-05-01 15:59:56.000000,2023-05-01 15:59:22,9.0,,1.0,2.0,1.0,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+282,gkx: Green-Kubo Method in JAX,,rep-learn,MIT,https://github.com/sirmarcel/gkx,Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast.,1,False,['transport-phenomena'],sirmarcel/gkx,https://github.com/sirmarcel/gkx,2023-04-30 12:25:16,2023-04-30 14:14:57.000000,2023-04-30 14:14:46,2.0,,,1.0,,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+283,kdft,,ml-dft,,https://gitlab.com/jmargraf/kdf,The Kernel Density Functional (KDF) code allows generating ML based DFT functionals.,1,False,,,,2020-11-07 21:50:22,2020-11-07 21:50:22.000000,,,,0.0,,,,,2.0,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,jmargraf/kdf,https://gitlab.com/jmargraf/kdf,
+284,MALA Tutorial,,educational,,https://github.com/mala-project/mala_tutorial,A full MALA hands-on tutorial.,1,False,,mala-project/mala_tutorial,https://github.com/mala-project/mala_tutorial,2023-03-09 14:01:54,2023-03-20 12:13:27.000000,2023-03-20 12:13:17,23.0,,,2.0,,,,1.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
+285,Descriptor Embedding and Clustering for Atomisitic-environment Framework (DECAF),,unsupervised,,https://gitlab.mpcdf.mpg.de/klai/decaf,Provides a workflow to obtain clustering of local environments in dataset of structures.,0,False,,,,,,,41.0,,,,,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
+286,SphericalNet,,rep-learn,,https://github.com/risilab/SphericalNet,Implementation of Clebsch-Gordan Networks (CGnet: https://arxiv.org/pdf/1806.09231.pdf) by GElib & cnine libraries in..,0,False,,risilab/SphericalNet,https://github.com/risilab/SphericalNet,2022-05-31 14:39:05,2022-06-07 03:57:10.000000,2022-06-07 03:53:49,1.0,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+287,KmdPlus,,unsupervised,,https://github.com/Minoru938/KmdPlus,"This module contains a class for treating kernel mean descriptor (KMD), and a function for generating descriptors with..",0,False,,Minoru938/KmdPlus,https://github.com/Minoru938/KmdPlus,2023-03-26 10:06:34,2023-03-26 11:03:39.000000,2023-03-26 11:03:39,4.0,,,1.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+288,GitHub topic materials-informatics,,community,,https://github.com/topics/materials-informatics,,0,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+289,MateriApps,,community,,https://ma.issp.u-tokyo.ac.jp/en/,,0,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,,,,,,,
+290,MLDensity,,ml-dft,,{},Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure..,0,False,,StefanoSanvitoGroup/MLdensity,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/latest-changes.md b/latest-changes.md
index e271399..0589a42 100644
--- a/latest-changes.md
+++ b/latest-changes.md
@@ -1,27 +1,43 @@
-## 📈 Trending Up
-
-_Projects that have a higher project-quality score compared to the last update. There might be a variety of reasons, such as increased downloads or code activity._
-
-- FLARE (🥇19 · ⭐ 240 · 📈) - An open-source Python package for creating fast and accurate interatomic potentials. MIT C++ MLIAP
-- Uni-Mol (🥈17 · ⭐ 400 · 📈) - Official Repository for the Uni-Mol Series Methods. MIT pre-trained
-- PyXtalFF (🥈15 · ⭐ 71 · 📈) - Machine Learning Interatomic Potential Predictions. MIT
-- UVVisML (🥉8 · ⭐ 11 · 📈) - Predict optical properties of molecules with machine learning. MIT optical-properties single-paper probabilistic
-- BOSS (🥈7 · ⭐ 18 · 💀) - Bayesian Optimization Structure Search (BOSS). Unlicensed probabilistic
-
-## 📉 Trending Down
-
-_Projects that have a lower project-quality score compared to the last update. There might be a variety of reasons such as decreased downloads or code activity._
-
-- iam-notebooks (🥈8 · ⭐ 16 · 📉) - Jupyter notebooks for the lectures of the Introduction to Atomistic Modeling. Apache-2
-
## ➕ Added Projects
_Projects that were recently added to this best-of list._
-- Best-of Machine Learning with Python (🥇22 · ⭐ 14K · ➕) - A ranked list of awesome machine learning Python libraries. Updated weekly. CC-BY-4.0 general-ml Python
-- Graph-based Deep Learning Literature (🥈18 · ⭐ 4.3K · ➕) - links to conference publications in graph-based deep learning. MIT general-ml rep-learn
-- Awesome Materials Informatics (🥉11 · ⭐ 290 · ➕) - Curated list of known efforts in materials informatics = modern materials science. Custom t o p i c s / m a t e r i a l s - i n f o r m a t i c s
-- The Collection of Database and Dataset Resources in Materials Science (🥉8 · ⭐ 160 · ➕) - A list of databases, datasets and books/handbooks where you can find materials properties for machine learning.. Unlicensed datasets
-- A Highly Opinionated List of Open-Source Materials Informatics Resources (🥉7 · ⭐ 93 · 💀) - A Highly Opinionated List of Open Source Materials Informatics Resources. MIT
-- GitHub topic materials-informatics (➕) - Unlicensed
+- cdk (🥇25 · ⭐ 430 · ➕) - The Chemistry Development Kit. LGPL-2.1 cheminformatics Java
+- MPContribs (🥇23 · ⭐ 32 · ➕) - Platform for materials scientists to contribute and disseminate their materials data through Materials Project. MIT
+- Open Catalyst datasets (🥇18 · ⭐ 450 · ➕) - The datasets of the Open Catalyst project, OC20, OC22. CC-BY-4.0
+- GT4SD (🥇18 · ⭐ 230 · ➕) - GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process. MIT pre-trained drug-discovery rep-learn
+- CHGNet (🥈18 · ⭐ 79 · 🐣) - Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov. Custom MD pre-trained electrostatics magnetism structure-relaxation
+- escnn (🥈17 · ⭐ 200 · ➕) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. Custom
+- MatBench (🥈16 · ⭐ 77 · ➕) - Matbench: Benchmarks for materials science property prediction. MIT datasets benchmarking
+- NNPOps (🥈15 · ⭐ 61 · ➕) - High-performance operations for neural network potentials. MIT MD C++
+- AI for Science Resources (🥉14 · ⭐ 220 · 🐣) - List of resources for AI4Science research, including learning resources. GPL-3.0 license
+- Artificial Intelligence for Science (AIRS) (🥉14 · ⭐ 220 · 🐣) - Artificial Intelligence for Science (AIRS). GPL-3.0 license rep-learn generative MLIAP MD ML-DFT ML-WFT biomolecules
+- openmm-torch (🥈14 · ⭐ 130 · ➕) - OpenMM plugin to define forces with neural networks. Custom MLIAP C++
+- SPICE (🥈14 · ⭐ 89 · ➕) - A collection of QM data for training potential functions. MIT MLIAP MD
+- mp-pyrho (🥉14 · ⭐ 27 · ➕) - Custom ML-DFT
+- GlassPy (🥈13 · ⭐ 14 · ➕) - Python module for scientists working with glass materials. GPL-3.0
+- mat2vec (🥈12 · ⭐ 590 · ➕) - Supplementary Materials for Tshitoyan et al. Unsupervised word embeddings capture latent knowledge from materials.. MIT rep-learn
+- TensorMol (🥈12 · ⭐ 260 · 💀) - Tensorflow + Molecules = TensorMol. GPL-3.0 single-paper
+- OpenMM-ML (🥉10 · ⭐ 50 · ➕) - High level API for using machine learning models in OpenMM simulations. MIT MLIAP
+- ai4material_design (🥈10 · ⭐ 1 · ➕) - Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of.. Apache-2 pre-trained material-defect
+- EDM (🥉9 · ⭐ 290 · 💀) - E(3) Equivariant Diffusion Model for Molecule Generation in 3D. MIT
+- PROPhet (🥈9 · ⭐ 59 · 💀) - PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches. GPL-3.0 MLIAP MD single-paper C++
+- escnn_jax (🥉8 · ⭐ 21 · 🐣) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. Custom
+- SkipAtom (🥈8 · ⭐ 21 · 💀) - Distributed representations of atoms, inspired by the Skip-gram model. MIT
+- wfl (🥉8 · ⭐ 13 · ➕) - Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows. Unlicensed workflows HTC
+- EquiformerV2 (🥉6 · ⭐ 62 · 🐣) - [arXiv23] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. MIT
+- MEGAN (🥈6 · ⭐ 1 · ➕) - Minimal implementation of graph attention student model architecture. MIT XAI rep-learn
+- tensorfieldnetworks (🥉5 · ⭐ 140 · 💀) - MIT
+- EquivariantOperators.jl (🥉5 · ⭐ 17 · 💤) - MIT Julia
+- SciGlass (🥉5 · ⭐ 6 · ➕) - The database contains a vast set of data on the properties of glass materials. MIT
+- CraTENet (🥉5 · ⭐ 6 · ➕) - An attention-based deep neural network for thermoelectric transport properties. MIT transport-phenomena
+- closed-loop-acceleration-benchmarks (🥈5 · ➕) - Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational.. MIT materials-discovery active-learning single-paper
+- Closed-loop acceleration benchmarks (🥈5 · ➕) - Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational.. MIT materials-discovery active-learning single-paper
+- ML-for-CurieTemp-Predictions (🥉5 · 🐣) - Machine Learning Predictions of High-Curie-Temperature Materials. MIT single-paper magnetism
+- Linear vs blackbox (🥉4 · 💤) - Code and data related to the publication: Interpretable models for extrapolation in scientific machine learning. MIT XAI single-paper rep-eng
+- Atom2Vec (🥉3 · ⭐ 24 · 💀) - Atom2Vec: a simple way to describe atoms for machine learning. Unlicensed
+- sl_discovery (🥉3 · ⭐ 5 · 💤) - Data processing and models related to Quantifying the performance of machine learning models in materials discovery. Apache-2 materials-discovery single-paper
+- Element encoder (🥉3 · ⭐ 5 · 💀) - Autoencoder neural network to compress properties of atomic species into a vector representation. GPL-3.0 single-paper
+- DeepCDP (🥉3 · ⭐ 2 · ➕) - DeepCDP: Deep learning Charge Density Prediction. Unlicensed
+- MateriApps (➕) - Unlicensed