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ML4Chem: A Machine Learning Package for Chemistry and Materials Science

preprint
submitted on 06.03.2020 and posted on 09.03.2020 by Muammar El Khatib, Wibe de Jong
ML4Chem is an open-source machine learning library for chemistry and materials science. It provides an extendable platform to develop and deploy machine learning models and pipelines and is targeted to the non-expert and expert users. ML4Chem follows user-experience design and offers the needed tools to go from data preparation to inference. Here we introduce its atomistic module for the implementation, deployment, and reproducibility of atom-centered models. This module is composed of six core building blocks: data, featurization, models, model optimization, inference, and visualization. We present their functionality and ease of use with demonstrations utilizing neural networks and kernel ridge regression algorithms.

Funding

Laboratory Directed Research and Development of Lawrence Berkeley National Laboratory

History

Email Address of Submitting Author

melkhatibr@lbl.gov

Institution

Lawrence Berkeley National Laboratory

Country

United States

ORCID For Submitting Author

https://orcid.org/0000-0002-0977-8439

Declaration of Conflict of Interest

No conflict of interest.

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