Update README.md

README.md

@@ -6,8 +6,6 @@ MAST-ML is an open-source Python package designed to broaden and accelerate the
 
 <img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/mastml">
 
-[![Actions Status](https://github.com/uw-cmg/MAST-ML/tree/master/.github/workflows/python_package.yml/badge.svg)](https://github.com/uw-cmg/MAST-ML/actions)
-
 <a href='https://mastmldocs.readthedocs.io/en/latest/?badge=latest'>
     <img src='https://readthedocs.org/projects/mastmldocs/badge/?version=latest' alt='Documentation Status' />
 </a>
@@ -35,38 +33,14 @@ MAST-ML is an open-source Python package designed to broaden and accelerate the
 * Tutorial 7: Model predictions with guide rails:
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uw-cmg/MAST-ML/blob/master/examples/MASTML_Tutorial_7_ModelPredictions_with_Guide_Rails.ipynb)
 
-## MAST-ML version 3.1.x Major Updates from July 2022
-* Refinement of tutorials, addition of Tutorial 7, Colab links as badges added for easier use.
-
-* mastml_predictor module added to help streamline making predictions (with option to include error bars) on new test data.
-
-* Basic parallelization added, which is especially useful for speeding up nested CV runs with many inner splits.
-
-* EnsembleModel now handles ensembles of GPR and XGBoost models.
-
-* Numerous improvements to plotting, including new plots (QQ plot), better axis handling and error bars (RvE plot), plotting and stats separated per group if groups are specified.
-
-* Improvements to feature selection methods. EnsembleModelFeatureSelector includes dummy feature references, added SHAP-based selector
-
-* Added assessment of baseline tests like comparing metrics to predicting the data average or permuted data test
-
-* Many miscellaneous bug fixes.
-
-
-## MAST-ML version 3.0.x Major Updates from July 2021
-* MAST-ML no longer uses an input file. The core functionality and workflow of MAST-ML has been rewritten to be more conducive to use in a Jupyter notebook environment. This major change has made the code more modular and transparent, and we believe more intuitive and easier to use in a research setting. The last version of MAST-ML to have input file support was version 2.0.20 on PyPi.
-
-* Each component of MAST-ML can be run in a Jupyter notebook environment, either locally or through a cloud-based service like Google Colab. As a result, we have completely reworked our use-case tutorials and examples. All of these MAST-ML tutorials are in the form of Jupyter notebooks and can be found in the mastml/examples folder on Github.
-
-* An active part of improving MAST-ML is to provide an automated, quantitative analysis of model domain assessement and model prediction uncertainty quantification (UQ). Version 3.x of MAST-ML includes more detailed implementation of model UQ using new and established techniques.
-
 ## Contributors
 
 University of Wisconsin-Madison Computational Materials Group:
 * Prof. Dane Morgan
 * Dr. Ryan Jacobs
+* Lane Schultz
 * Dr. Tam Mayeshiba
-* Ben Afflerbach
+* Dr. Ben Afflerbach
 * Dr. Henry Wu
 
 University of Kentucky contributors:
@@ -84,32 +58,72 @@ https://mastmldocs.readthedocs.io/en/latest/
 
 ## Funding
 
-This work was and is funded by the National Science Foundation (NSF) SI2 award No. 1148011 and DMREF award number DMR-1332851
+This work was funded by the National Science Foundation (NSF) SI2 award number 1148011
 
+This work was funded by the National Science Foundation (NSF) DMREF award number DMR-1332851
+
+This work was funded by the National Science Foundation (NSF) CSSI award number 1931298
 
-## Citing MAST-ML
 
+## Citing MAST-ML
 
 If you find MAST-ML useful, please cite the following publication:
 
 Jacobs, R., Mayeshiba, T., Afflerbach, B., Miles, L., Williams, M., Turner, M., Finkel, R., Morgan, D.,
 "The Materials Simulation Toolkit for Machine Learning (MAST-ML): An automated open source toolkit to accelerate data-
 driven materials research", Computational Materials Science 175 (2020), 109544. https://doi.org/10.1016/j.commatsci.2020.109544
 
+If you find the uncertainty quantification (error bar) approaches useful, please cite the following publication:
 
+Palmer, G., Du, S., Politowicz, A., Emory, J. P., Yang, X., Gautam, A., Gupta, G., Li, Z., Jacobs, R., Morgan, D.,
+"Calibration after bootstrap for accurate uncertainty quantification in regression models", npj Computational Materials 8 115 (2022). https://doi.org/10.1038/s41524-022-00794-8
 
-## Code Repository
+## Installation
 
-MAST-ML is freely available via Github: 
+MAST-ML can be installed via pip:
 
 ```
-https://github.com/uw-cmg/MAST-ML
-
-git clone https://github.com/uw-cmg/MAST-ML
+pip install mastml
 ```
 
-MAST-ML can also be installed via pip:
+Clone from Github: 
 
 ```
-pip install mastml
+git clone https://github.com/uw-cmg/MAST-ML
 ```
+
+# Changelog
+
+## MAST-ML version 3.2.x Major Updates from April 2024
+* Integration of domain of applicability approach using kernel density estimates based on MADML package: https://github.com/leschultz/materials_application_domain_machine_learning
+
+* Refinement of tutorials, addition of new tutorial for domains of applicability
+
+* Updates to plotting routines for error bar analysis
+
+* Many small bug fixes and updates to conform to updated versions of package dependencies
+
+## MAST-ML version 3.1.x Major Updates from July 2022
+* Refinement of tutorials, addition of Tutorial 7, Colab links as badges added for easier use.
+
+* mastml_predictor module added to help streamline making predictions (with option to include error bars) on new test data.
+
+* Basic parallelization added, which is especially useful for speeding up nested CV runs with many inner splits.
+
+* EnsembleModel now handles ensembles of GPR and XGBoost models.
+
+* Numerous improvements to plotting, including new plots (QQ plot), better axis handling and error bars (RvE plot), plotting and stats separated per group if groups are specified.
+
+* Improvements to feature selection methods. EnsembleModelFeatureSelector includes dummy feature references, added SHAP-based selector
+
+* Added assessment of baseline tests like comparing metrics to predicting the data average or permuted data test
+
+* Many miscellaneous bug fixes.
+
+
+## MAST-ML version 3.0.x Major Updates from July 2021
+* MAST-ML no longer uses an input file. The core functionality and workflow of MAST-ML has been rewritten to be more conducive to use in a Jupyter notebook environment. This major change has made the code more modular and transparent, and we believe more intuitive and easier to use in a research setting. The last version of MAST-ML to have input file support was version 2.0.20 on PyPi.
+
+* Each component of MAST-ML can be run in a Jupyter notebook environment, either locally or through a cloud-based service like Google Colab. As a result, we have completely reworked our use-case tutorials and examples. All of these MAST-ML tutorials are in the form of Jupyter notebooks and can be found in the mastml/examples folder on Github.
+
+* An active part of improving MAST-ML is to provide an automated, quantitative analysis of model domain assessement and model prediction uncertainty quantification (UQ). Version 3.x of MAST-ML includes more detailed implementation of model UQ using new and established techniques.