ChemML

ChemML is a machine learning and informatics program suite for the analysis, mining, and modeling of chemical and materials data.

ChemML is developed in the Python 3 programming language and makes use of a host of data analysis and ML libraries, as well as domain-specific libraries. The development follows a strictly modular and object-oriented design to make the overall code as flexible and versatile as possible.

Please check the ChemML website for more information.

Latest Version:

• to find out about the latest version and release history, click here
• for a detailed list of changes, see the Changelog

Installation and Dependencies:

We strongly recommend you to install ChemML in an Anaconda environment. The instructions to create the environment, install ChemML’s dependencies, and subsequently install ChemML are as follows:

conda create --name chemml_env python=3.13
source activate chemml_env
<!-- Make sure to run this command in a clean location -->
git clone https://github.com/hachmannlab/chemml.git
pip install ./chemml

Here is a list of external libraries that will be installed with chemml:

• numpy
• pandas
• tensorflow
• rdkit
• scikit-learn
• matplotlib
• seaborn
• lxml
• openpyxl
• ipywidgets
• shap
• lime
• openbabel
  • (NOTE: Python 3.8 requires a separate conda install of openbabel) conda install -c conda-forge openbabel
• torch
• torchvision

Optional dependencies (user-installed):

• python-graphviz, nb_conda_kernels (for ChemML Jupyter wrapper)
• xgboost, lightgbm (for AutoML screening)
• Additional PyTorch libraries depending on operating system and GPU configuration, see here.

Citation:

Please cite the use of ChemML as:

Main citation:

@article{haghighatlari2019chemml,
    title        = {{ChemML}: A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data},
    author       = {Haghighatlari, Mojtaba and Vishwakarma, Gaurav and Altarawy, Doaa and Subramanian, Ramachandran and Kota, Bhargava U and Sonpal, Aditya and Setlur, Srirangaraj and Hachmann, Johannes},
    year         = 2020,
    journal      = {Wiley Interdisciplinary Reviews: Computational Molecular Science},
    publisher    = {Wiley Online Library},
    volume       = 10,
    doi          = {https://doi.org/10.1002/wcms.1458},
    pages        = {e1458},
}


Other references:

@article{chemml_review2019,
author = {Haghighatlari, Mojtaba and Hachmann, Johannes},
doi = {https://doi.org/10.1016/j.coche.2019.02.009},
issn = {2211-3398},
journal = {Current Opinion in Chemical Engineering},
month = {jan},
pages = {51--57},
title = {Advances of machine learning in molecular modeling and simulation},
volume = {23},
year = {2019}
}

@article{Hachmann2018,
author = {Hachmann, Johannes and Afzal, Mohammad Atif Faiz and Haghighatlari, Mojtaba and Pal, Yudhajit},
doi = {10.1080/08927022.2018.1471692},
issn = {10290435},
journal = {Molecular Simulation},
number = {11},
pages = {921--929},
title = {Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space},
volume = {44},
year = {2018}
}

@article{vishwakarma2019towards,
title={Towards autonomous machine learning in chemistry via evolutionary algorithms},
author={Vishwakarma, Gaurav and Haghighatlari, Mojtaba and Hachmann, Johannes},
journal={ChemRxiv preprint},
year={2019}
}

License:

ChemML is copyright (C) 2014-2026 Johannes Hachmann and Mojtaba Haghighatlari, Aditya Sonpal, Gaurav Vishwakarma, Aatish Pradhan and Nitin Murthy all rights reserved. ChemML is distributed under 3-Clause BSD License (https://opensource.org/licenses/BSD-3-Clause).

About us:

Maintainers:

- Johannes Hachmann, hachmann@buffalo.edu
- Mojtaba Haghighatlari
- Aditya Sonpal, adityaso@buffalo.edu
- Aatish Pradhan, aatishpr@buffalo.edu
- Nitin Murthy, nitinmad@buffalo.edu
University at Buffalo - The State University of New York (UB)

Contributors:

- Doaa Altarawy (MolSSI): scientific advice and software mentor 
- Aditya Sonpal (UB): graph convolution NNs, XAI
- Aatish Pradhan (UB): autoML and Jupyter GUI developer
- Gaurav Vishwakarma (UB): automated model optimization
- Ramachandran Subramanian (UB): Magpie descriptor library port
- Bhargava Urala Kota (UB): library database
- Srirangaraj Setlur (UB): scientific advice
- Venugopal Govindaraju (UB): scientific advice
- Krishna Rajan (UB): scientific advice


- We encourage any contributions and feedback. Feel free to fork and make pull-request to the "development" branch.

Acknowledgements:

- ChemML is based upon work supported by the U.S. National Science Foundation under grant #OAC-1751161 and in part by #OAC-1640867.
- ChemML was also supported by start-up funds provided by UB's School of Engineering and Applied Science and UB's Department of Chemical and Biological Engineering, the New York State Center of Excellence in Materials Informatics through seed grant #1140384-8-75163, and the U.S. Department of Energy under grant #DE-SC0017193.
- Mojtaba Haghighatlari received 2018 Phase-I and 2019 Phase-II Software Fellowships by the Molecular Sciences Software Institute (MolSSI) for his work on ChemML.