吸附
密度泛函理论
可转让性
纳米技术
化学物理
材料科学
化学
物理
计算化学
计算机科学
机器学习
物理化学
罗伊特
作者
Sheng Rong Gong,Shuo Wang,Taishan Zhu,Xi Chen,Zhenze Yang,Markus J. Buehler,Yang Shao‐Horn,Jeffrey C. Grossman
出处
期刊:JACS Au
[American Chemical Society]
日期:2021-10-06
卷期号:1 (11): 1904-1914
被引量:31
标识
DOI:10.1021/jacsau.1c00260
摘要
and substrate, and the dependence is used to screen all 2D metallic materials. Physics-simplified learning by splitting the property into different contributions and learning or calculating each component is shown to have higher accuracy and transferability for machine learning of complex materials properties.
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