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.
Retraction Note to: Urban land ecological evaluation and English translation model optimization based on machine learning
