Infusing theory into deep learning for interpretable reactivity prediction
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DOI: 10.1038/s41467-021-25639-8
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Cited by:
- Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
- Gang Wang & Shinya Mine & Duotian Chen & Yuan Jing & Kah Wei Ting & Taichi Yamaguchi & Motoshi Takao & Zen Maeno & Ichigaku Takigawa & Koichi Matsushita & Ken-ichi Shimizu & Takashi Toyao, 2023. "Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
- Hemanth Somarajan Pillai & Yi Li & Shih-Han Wang & Noushin Omidvar & Qingmin Mu & Luke E. K. Achenie & Frank Abild-Pedersen & Juan Yang & Gang Wu & Hongliang Xin, 2023. "Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
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