Machine Learning Potential Repository

Abstract

This paper introduces a machine learning potential repository that includes Pareto optimal machine learning potentials. It also shows the systematic development of accurate and fast machine learning potentials for a wide range of elemental systems. As a result, many Pareto optimal machine learning potentials are available in the repository from a website. Therefore, the repository will help many scientists to perform accurate and fast atomistic simulations.

arXiv comment9 pages, 3 figures

Cited by in corpus (1)

• Application of machine learning potentials to predict grain boundary properties in fcc elemental metals