Table of Contents

• Introduction
• Installation
• Datasets
• Models
• Tasks
• Developing

Introduction

AtomGen provides a robust framework for handling atomistic graph datasets focusing on transformer-based implementations. We provide utilities for training various models, experimenting with different pre-training tasks, and pre-trained models and datasets on huggingface hub.

It streamlines the process of aggregation, standardization, and utilization of datasets from diverse sources, enabling large-scale pre-training and generative modeling on atomistic graphs.

Installation

The package can be installed using poetry:

python3 -m poetry install
source $(poetry env info --path)/bin/activate

Datasets

AtomGen facilitates the aggregation and standardization of datasets, including but not limited to:

• S2EF Datasets: Aggregated from multiple sources such as OC20, OC22, ODAC23, MPtrj, and SPICE with structures and energies/forces for pre-training.

• Misc. Atomistic Graph Datasets: Including Molecule3D, Protein Data Bank (PDB), and the Open Quantum Materials Database (OQMD).

Currently, AtomGen has pre-processed datasets for the S2EF pre-training task for OC20 and a mixed dataset of OC20, OC22, ODAC23, MPtrj, and SPICE. They have been uploaded to huggingface hub and can be accessed using the datasets API.

Models

AtomGen supports a variety of models for training on atomistic graph datasets, including:

• AtomFormer: Custom architecture that leverages gaussian pair-wise positional embeddings and self-attention to model atomistic graphs.
• SchNet: A continuous-filter convolutional neural network for modeling quantum interactions.
• TokenGT: Tokenized graph transformer that treats all nodes and edges as independent tokens.

Tasks

Experimentation with pre-training tasks is facilitated through AtomGen, including:

• Structure to Energy & Forces: Predicting energies and forces for atomistic graphs.

• Masked Atom Modeling: Masking atoms and predicting their properties.

• Coordinate Denoising: Denoising atom coordinates.

These tasks are all facilitated through the DataCollatorForAtomModeling class and can be used simultaneously or individually.

🧑🏿‍💻 Developing

Installing dependencies

The development environment can be set up using poetry. Hence, make sure it is installed and then run:

python3 -m poetry install
source $(poetry env info --path)/bin/activate

In order to install dependencies for testing (codestyle, unit tests, integration tests), run:

python3 -m poetry install --with test