Year: 2020
Type: preprint
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.
Source: arXiv (Cornell University)
Author: Seko, Atsuto
Institution: Kyoto University
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3045911943