Nature Communications (May 2022)

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,
  • Takeshi Ibuka

DOI
https://doi.org/10.1038/s41467-022-30687-9
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 11

Abstract

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Existing neural network potentials are generally designed for narrow target materials. Here the authors develop a neural network potential which is able to handle any combination of 45 elements and show its applicability in multiple domains.