Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements

Author:

Takamoto SoORCID,Shinagawa ChikashiORCID,Motoki DaisukeORCID,Nakago KosukeORCID,Li Wenwen,Kurata IoriORCID,Watanabe Taku,Yayama YoshihiroORCID,Iriguchi Hiroki,Asano Yusuke,Onodera Tasuku,Ishii Takafumi,Kudo Takao,Ono Hideki,Sawada Ryohto,Ishitani Ryuichiro,Ong Marc,Yamaguchi Taiki,Kataoka Toshiki,Hayashi AkihideORCID,Charoenphakdee NontawatORCID,Ibuka TakeshiORCID

Abstract

AbstractComputational material discovery is under intense study owing to its ability to explore the vast space of chemical systems. Neural network potentials (NNPs) have been shown to be particularly effective in conducting atomistic simulations for such purposes. However, existing NNPs are generally designed for narrow target materials, making them unsuitable for broader applications in material discovery. Here we report a development of universal NNP called PreFerred Potential (PFP), which is able to handle any combination of 45 elements. Particular emphasis is placed on the datasets, which include a diverse set of virtual structures used to attain the universality. We demonstrated the applicability of PFP in selected domains: lithium diffusion in LiFeSO4F, molecular adsorption in metal-organic frameworks, an order–disorder transition of Cu-Au alloys, and material discovery for a Fischer–Tropsch catalyst. They showcase the power of PFP, and this technology provides a highly useful tool for material discovery.

Publisher

Springer Science and Business Media LLC

Subject

General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary

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