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ML4Chem: Machine Learning for Chemistry and Materials v0.0.1

RESOURCE

Publicly Accessible Repository
https://github.com/muammar/ml4chem
https://doi.org/10.11578/dc.20191002.3

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Abstract

ML4Chem is a free, and open-source machine learning package for chemistry and materials sciences written in Python. It allows scientists to focus on applying the most commonly used machine learning models in chemistry and materials sciences with minimum effort.
Developers:
de Jong, Wibe [1] ;  El Khatib Rodriguez, Muammar [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Release Date:
2019-10-02
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Licenses:
BSD 3-clause "New" or "Revised" License
Sponsoring Org.:
Code ID:
28653
Site Accession Number:
2019-130
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Country of Origin:
United States

RESOURCE

Publicly Accessible Repository
https://github.com/muammar/ml4chem
https://doi.org/10.11578/dc.20191002.3

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Citation Formats

de Jong, Wibe, and El Khatib Rodriguez, Muammar. ML4Chem: Machine Learning for Chemistry and Materials v0.0.1. Computer Software. https://github.com/muammar/ml4chem. USDOE. 02 Oct. 2019. Web. doi:10.11578/dc.20191002.3.
de Jong, Wibe, & El Khatib Rodriguez, Muammar. (2019, October 02). ML4Chem: Machine Learning for Chemistry and Materials v0.0.1. [Computer software]. https://github.com/muammar/ml4chem. https://doi.org/10.11578/dc.20191002.3.
de Jong, Wibe, and El Khatib Rodriguez, Muammar. "ML4Chem: Machine Learning for Chemistry and Materials v0.0.1." Computer software. October 02, 2019. https://github.com/muammar/ml4chem. https://doi.org/10.11578/dc.20191002.3.
@misc{ doecode_28653,
title = {ML4Chem: Machine Learning for Chemistry and Materials v0.0.1},
author = {de Jong, Wibe and El Khatib Rodriguez, Muammar},
abstractNote = {ML4Chem is a free, and open-source machine learning package for chemistry and materials sciences written in Python. It allows scientists to focus on applying the most commonly used machine learning models in chemistry and materials sciences with minimum effort.},
doi = {10.11578/dc.20191002.3},
url = {https://doi.org/10.11578/dc.20191002.3},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20191002.3}},
year = {2019},
month = {oct}
}