Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning
Name
gong-et-al-2022.pdf
Description
Published version
Size
3.16 MB
Format
Adobe PDF
Checksum (MD5)
bea51581edf8b6d8d861bebe100ad53b
Author(s)
Gong, Sheng
Wang, Shuo
Xie, Tian
Chae, Woo Hyun
Liu, Runze
Shao-Horn, Yang
Grossman, Jeffrey C.
Date Issued
September 9, 2022
Journal
JACS Au
Publisher
American Chemical Society
Citation
Sheng Gong, Shuo Wang, Tian Xie, Woo Hyun Chae, Runze Liu, Yang Shao-Horn, and Jeffrey C. Grossman
JACS Au 2022 2 (9), 1964-1977.
Version
Final published version
Abstract
The application of machine learning to predict materials properties measured by experiments are valuable yet difficult due to the limited amount of experimental data. In this work, we use a multifidelity random forest model to learn the experimental formation enthalpy of materials with prediction accuracy higher than the Perdew-Burke-Ernzerhof (PBE) functional with linear correction, PBEsol, and meta-generalized gradient approximation (meta-GGA) functionals (SCAN and r2SCAN), 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 multifidelity model is also used as a data-mining approach to find how DFT deviates from experiments by explaining the model output.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/jacsau.2c00235
