Exploiting redundancy in large materials datasets for efficient machine learning with less data
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DOI: 10.1038/s41467-023-42992-y
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- Xiwen Jia & Allyson Lynch & Yuheng Huang & Matthew Danielson & Immaculate Lang’at & Alexander Milder & Aaron E. Ruby & Hao Wang & Sorelle A. Friedler & Alexander J. Norquist & Joshua Schrier, 2019. "Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis," Nature, Nature, vol. 573(7773), pages 251-255, September.
- Miao Zhong & Kevin Tran & Yimeng Min & Chuanhao Wang & Ziyun Wang & Cao-Thang Dinh & Phil De Luna & Zongqian Yu & Armin Sedighian Rasouli & Peter Brodersen & Song Sun & Oleksandr Voznyy & Chih-Shan Ta, 2020. "Accelerated discovery of CO2 electrocatalysts using active machine learning," Nature, Nature, vol. 581(7807), pages 178-183, May.
- 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.
- Keith T. Butler & Daniel W. Davies & Hugh Cartwright & Olexandr Isayev & Aron Walsh, 2018. "Machine learning for molecular and materials science," Nature, Nature, vol. 559(7715), pages 547-555, July.
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