To address surface reaction network complexity using scaling relations machine learning and DFT calculations
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DOI: 10.1038/ncomms14621
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Cited by:
- Shunsaku Yasumura & Kenichiro Saita & Takumi Miyakage & Ken Nagai & Kenichi Kon & Takashi Toyao & Zen Maeno & Tetsuya Taketsugu & Ken-ichi Shimizu, 2023. "Designing main-group catalysts for low-temperature methane combustion by ozone," Nature Communications, Nature, vol. 14(1), pages 1-11, 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.
- Xiyang Wang & Qilei Yang & Xinbo Li & Zhen Li & Chuan Gao & Hui Zhang & Xuefeng Chu & Carl Redshaw & Shucheng Shi & Yimin A. Wu & Yongliang Ma & Yue Peng & Junhua Li & Shouhua Feng, 2024. "Exploring the dynamic evolution of lattice oxygen on exsolved-Mn2O3@SmMn2O5 interfaces for NO Oxidation," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
- 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.
- Damilola Ologunagba & Shyam Kattel, 2020. "Machine Learning Prediction of Surface Segregation Energies on Low Index Bimetallic Surfaces," Energies, MDPI, vol. 13(9), pages 1-13, May.
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