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 easiness of use with demonstrations utilizing neural networks and kernel ridge regression algorithms.
@article{arxiv.2003.13388,
title = {ML4Chem: A Machine Learning Package for Chemistry and Materials Science},
author = {Muammar El Khatib and Wibe A de Jong},
journal= {arXiv preprint arXiv:2003.13388},
year = {2020}
}