Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements
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DOI: 10.1038/s41467-022-30687-9
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References listed on IDEAS
- 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.
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- Katja-Sophia Csizi & Miguel Steiner & Markus Reiher, 2024. "Nanoscale chemical reaction exploration with a quantum magnifying glass," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
- 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.
- 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.
- 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.
- 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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