ML4Chem: A Machine Learning Package for Chemistry and Materials Science
This package addresses the need for accessible tools in chemistry and materials science for both non-expert and expert users, but it is incremental as it builds on existing methods.
The authors introduced ML4Chem, an open-source machine learning library for chemistry and materials science, providing an extendable platform with an atomistic module for implementing and deploying atom-centered models, demonstrated using neural networks and kernel ridge regression.
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.