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

Author

Listed:
  • 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)

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.

Suggested Citation

  • So Takamoto & Chikashi Shinagawa & Daisuke Motoki & Kosuke Nakago & Wenwen Li & Iori Kurata & Taku Watanabe & Yoshihiro Yayama & Hiroki Iriguchi & Yusuke Asano & Tasuku Onodera & Takafumi Ishii & Taka, 2022. "Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30687-9
    DOI: 10.1038/s41467-022-30687-9
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    References listed on IDEAS

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    1. Yuki Yamada & Kenji Usui & Keitaro Sodeyama & Seongjae Ko & Yoshitaka Tateyama & Atsuo Yamada, 2016. "Hydrate-melt electrolytes for high-energy-density aqueous batteries," Nature Energy, Nature, vol. 1(10), pages 1-9, October.
    2. Xingfeng He & Yizhou Zhu & Yifei Mo, 2017. "Origin of fast ion diffusion in super-ionic conductors," Nature Communications, Nature, vol. 8(1), pages 1-7, August.
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    Cited by:

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    2. Miguel Steiner & Markus Reiher, 2024. "A human-machine interface for automatic exploration of chemical reaction networks," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    3. Andreas Erlebach & Martin Šípka & Indranil Saha & Petr Nachtigall & Christopher J. Heard & Lukáš Grajciar, 2024. "A reactive neural network framework for water-loaded acidic zeolites," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    4. Yuta Sakanaka & Shotaro Hiraide & Iori Sugawara & Hajime Uematsu & Shogo Kawaguchi & Minoru T. Miyahara & Satoshi Watanabe, 2023. "Generalised analytical method unravels framework-dependent kinetics of adsorption-induced structural transition in flexible metal–organic frameworks," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    5. Kangming Li & Daniel Persaud & Kamal Choudhary & Brian DeCost & Michael Greenwood & Jason Hattrick-Simpers, 2023. "Exploiting redundancy in large materials datasets for efficient machine learning with less data," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

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