Skip to main content

A Machine Learning and Informatics Program Suite for the Chemical and Materials Sciences

Project description

Build Status codecov Language grade: Python version status license

ChemML

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

ChemML

Code Design:

ChemML is developed in the Python 3 programming language and makes use of a host of data analysis and ML libraries(accessible through the Anaconda distribution), 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.

The format of library is similar to the well known libraries like Scikit-learn.

Latest Version:

  • to find out about the latest version and release history, click here

Installation and Dependencies:

You can download ChemML from PyPI via pip.

pip install chemml --user -U

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

  • numpy
  • pandas
  • tensorflow
  • scikit-learn
  • matplotlib
  • seaborn
  • lxml
  • openpyxl
  • ipywidgets

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 using the Python Package Index (PyPI) via pip are as follows:

conda create --name chemml_env python=3.8
source activate chemml_env
conda install -c conda-forge openbabel rdkit nb_conda_kernels python-graphviz
pip install chemml

Citation:

Please cite the use of ChemML as:

Main citation:

@article{chemml2019,
author = {Haghighatlari, Mojtaba and Vishwakarma, Gaurav and Altarawy, Doaa and Subramanian, Ramachandran and Kota, Bhargava Urala and Sonpal, Aditya and Setlur, Srirangaraj and Hachmann, Johannes},
journal = {ChemRxiv},
pages = {8323271},
title = {ChemML: A Machine Learning and Informatics Program Package for the Analysis, Mining, and Modeling of Chemical and Materials Data},
doi = {10.26434/chemrxiv.8323271.v1},
year = {2019}
}


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) 2014-2022 Johannes Hachmann and Mojtaba Haghighatlari, Aditya Sonpal, Gaurav Vishwakarma and Aatish Pradhan 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
University at Buffalo - The State University of New York (UB)

Contributors:

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

- 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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

chemml-1.1.tar.gz (563.4 kB view details)

Uploaded Source

Built Distribution

chemml-1.1-py3-none-any.whl (649.3 kB view details)

Uploaded Python 3

File details

Details for the file chemml-1.1.tar.gz.

File metadata

  • Download URL: chemml-1.1.tar.gz
  • Upload date:
  • Size: 563.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/32.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.62.3 importlib-metadata/4.10.1 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.8.12

File hashes

Hashes for chemml-1.1.tar.gz
Algorithm Hash digest
SHA256 311d37d88c2d4289a4b107152450f53164111dbac9aa951f638e01b3398d74c6
MD5 940eaf54ddcf16633b32529842ad8bd0
BLAKE2b-256 bb19a1e2a5f46c06d26b3d2a31fb008631c989cfde1504479fd241e7bdae5100

See more details on using hashes here.

File details

Details for the file chemml-1.1-py3-none-any.whl.

File metadata

  • Download URL: chemml-1.1-py3-none-any.whl
  • Upload date:
  • Size: 649.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/32.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.62.3 importlib-metadata/4.10.1 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.8.12

File hashes

Hashes for chemml-1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 7dd10677197231e7fa66438021131f905ef328d61c387550c575f7eaf2b2c2c1
MD5 10970cc3eae7995eb0c84116151c3e55
BLAKE2b-256 b3a90f30f6c90904a39464e35d61e571b2b45009581362243acf0f5121a3b58d

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page