理论(学习稳定性)
忠诚
焓
代表(政治)
计算机科学
人工神经网络
实验数据
机器学习
工作(物理)
人工智能
密度泛函理论
算法
热力学
化学
数学
计算化学
物理
统计
电信
政治
政治学
法学
作者
Sheng Gong,Shuo Wang,Tian Xie,Woo Hyun Chae,Runze Liu,Yang Shao‐Horn,Jeffrey C. Grossman
出处
期刊:JACS Au
[American Chemical Society]
日期:2022-09-09
卷期号:2 (9): 1964-1977
被引量:11
标识
DOI:10.1021/jacsau.2c00235
摘要
Machine learning materials properties measured by experiments is valuable yet difficult due to the limited amount of experimental data. In this work, we use a multi-fidelity random forest model to learn the experimental formation enthalpy of materials with prediction accuracy higher than the empirically corrected PBE functional (PBEfe) and meta-GGA functional (SCAN), and it outperforms the hotly studied deep neural-network based representation learning and transfer learning. We then use the model to calibrate the DFT formation enthalpy in the Materials Project database, and discover materials with underestimated stability. The multi-fidelity model is also used as a data-mining approach to find how DFT deviates from experiments by the explaining the model output.
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