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

So Takamoto (), Chikashi Shinagawa, Daisuke Motoki, Kosuke Nakago, Wenwen Li, Iori Kurata, Taku Watanabe, Yoshihiro Yayama, Hiroki Iriguchi, Yusuke Asano, Tasuku Onodera, Takafumi Ishii, Takao Kudo, Hideki Ono, Ryohto Sawada, Ryuichiro Ishitani, Marc Ong, Taiki Yamaguchi, Toshiki Kataoka, Akihide Hayashi, Nontawat Charoenphakdee and Takeshi Ibuka ()
Additional contact information
So Takamoto: Preferred Networks, Inc.
Chikashi Shinagawa: Preferred Networks, Inc.
Daisuke Motoki: Preferred Networks, Inc.
Kosuke Nakago: Preferred Networks, Inc.
Wenwen Li: Preferred Networks, Inc.
Iori Kurata: Preferred Networks, Inc.
Taku Watanabe: ENEOS Corporation
Yoshihiro Yayama: ENEOS Corporation
Hiroki Iriguchi: ENEOS Corporation
Yusuke Asano: ENEOS Corporation
Tasuku Onodera: ENEOS Corporation
Takafumi Ishii: ENEOS Corporation
Takao Kudo: ENEOS Corporation
Hideki Ono: ENEOS Corporation
Ryohto Sawada: Preferred Networks, Inc.
Ryuichiro Ishitani: Preferred Networks, Inc.
Marc Ong: Preferred Networks, Inc.
Taiki Yamaguchi: Preferred Networks, Inc.
Toshiki Kataoka: Preferred Networks, Inc.
Akihide Hayashi: Preferred Networks, Inc.
Nontawat Charoenphakdee: Preferred Networks, Inc.
Takeshi Ibuka: ENEOS Corporation

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract Computational 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.

Date: 2022
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DOI: 10.1038/s41467-022-30687-9

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