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

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 design...

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Detalles Bibliográficos
Autores principales: Takamoto, So, Shinagawa, Chikashi, Motoki, Daisuke, Nakago, Kosuke, Li, Wenwen, Kurata, Iori, Watanabe, Taku, Yayama, Yoshihiro, Iriguchi, Hiroki, Asano, Yusuke, Onodera, Tasuku, Ishii, Takafumi, Kudo, Takao, Ono, Hideki, Sawada, Ryohto, Ishitani, Ryuichiro, Ong, Marc, Yamaguchi, Taiki, Kataoka, Toshiki, Hayashi, Akihide, Charoenphakdee, Nontawat, Ibuka, Takeshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9151783/
https://www.ncbi.nlm.nih.gov/pubmed/35637178
http://dx.doi.org/10.1038/s41467-022-30687-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9151783/
https://www.ncbi.nlm.nih.gov/pubmed/35637178
http://dx.doi.org/10.1038/s41467-022-30687-9

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