Guest Editorial: Special Topic on Software for Atomistic Machine Learning
Matthias Rupp, Emine K\"u\c{c}\"ukbenli, G\'abor Cs\'anyi
TL;DR
This paper surveys various software tools and approaches for atomistic machine learning, highlighting recent developments and challenges in the field.
Contribution
It provides a comprehensive overview of software used in atomistic machine learning, identifying gaps and future directions.
Findings
Summarizes key software tools and frameworks.
Identifies challenges in software development for atomistic ML.
Highlights future research opportunities.
Abstract
A survey of the contributions to the Journal of Chemical Physics' Special Topic on Software for Atomistic Machine Learning.
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Taxonomy
TopicsMachine Learning in Materials Science