Abstract
Machine learning potentials (MLPs) developed from extensive datasets constructed from density functional theory calculations have become increasingly appealing to many researchers. This paper presents a framework of polynomial-based MLPs, called polynomial MLPs. The systematic development of accurate and computationally efficient polynomial MLPs for many elemental and binary alloy systems and their predictive powers for various properties are also demonstrated. Consequently, many polynomial MLPs are available in a repository website [A. Seko, Polynomial Machine Learning Potential Repository at Kyoto University, https://sekocha.github.io]. The repository will help many scientists perform accurate and efficient large-scale atomistic simulations and crystal structure searches.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
