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ML4Chem: A Machine Learning Package for Chemistry and Materials Science

submitted on 06.03.2020, 23:25 and posted on 09.03.2020, 12:58 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.


Laboratory Directed Research and Development of Lawrence Berkeley National Laboratory


Email Address of Submitting Author


Lawrence Berkeley National Laboratory


United States

ORCID For Submitting Author

Declaration of Conflict of Interest

No conflict of interest.