Screening and Understanding Li Adsorption on Two-Dimensional Metallic Materials by Learning Physics and Physics-Simplified Learning
Name
jacsau.1c00260.pdf
Description
Published version
Size
2.14 MB
Format
Adobe PDF
Checksum (MD5)
d8492a1217ac7d6f9de718ef639dc697
Author(s)
Gong, Sheng
Wang, Shuo
Zhu, Taishan
Chen, Xi
Yang, Zhenze
Buehler, Markus J
Shao-Horn, Yang
Grossman, Jeffrey C
Date Issued
2021
Journal
JACS Au
Publisher
American Chemical Society (ACS)
Citation
Gong, Sheng, Wang, Shuo, Zhu, Taishan, Chen, Xi, Yang, Zhenze et al. 2021. "Screening and Understanding Li Adsorption on Two-Dimensional Metallic Materials by Learning Physics and Physics-Simplified Learning." JACS Au, 1 (11).
Version
Final published version
Abstract
Understanding and broad screening Li interaction energetics with surfaces are key to the development of materials for a wide range of applications including Li-based electrochemical capacitors, Li sensors, Li separation membranes, and Li-ion batteries. In this work, we build a high-throughput screening scheme to screen Li adsorption energetics on 2D metallic materials. First, density functional theory and graph convolution networks are utilized to calculate the minimum Li adsorption energies for some 2D metallic materials. The data is then used to find a dependence of the minimum Li adsorption energies on the sum of ionization potential, work function of the 2D metal, and coupling energy between Li+ 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.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/JACSAU.1C00260
