ML4Chem: A Machine Learning Package for Chemistry and Materials Science

09 March 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

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.

Keywords

Software development
deep learning
support vector machines model
cheminformatics
Materials chemistry
semi-supervised learning
autoencoders

Supplementary weblinks

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