Machine Learning Potential Repository
Computational Physics
2020-07-29 v1 Materials Science
Chemical Physics
Data Analysis, Statistics and Probability
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.
Cite
@article{arxiv.2007.14206,
title = {Machine Learning Potential Repository},
author = {Atsuto Seko},
journal= {arXiv preprint arXiv:2007.14206},
year = {2020}
}
Comments
9 pages, 3 figures