ML4Chem: A Machine Learning Package for Chemistry and Materials Science release_hwiznd6vangbfe2s45vkcfsf6i

by Muammar El Khatib, Wibe de Jong

Released as a post by American Chemical Society (ACS).

2020  

Abstract

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.
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Date   2020-03-09
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