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Predicting charge density distribution of materials using a local-environment-based graph convolutional network

Authors :
Massachusetts Institute of Technology. Research Laboratory of Electronics
Gong, Sheng
Xie, Tian
Zhu, Taishan
Wang, Shuo
Fadel, Eric R.
Grossman, Jeffrey C.
Massachusetts Institute of Technology. Research Laboratory of Electronics
Gong, Sheng
Xie, Tian
Zhu, Taishan
Wang, Shuo
Fadel, Eric R.
Grossman, Jeffrey C.
Source :
APS
Publication Year :
2021

Abstract

The electron charge density distribution of materials is one of the key quantities in computational materials science as theoretically it determines the ground state energy and practically it is used in many materials analyses. However, the scaling of density functional theory calculations with number of atoms limits the usage of charge-density-based calculations and analyses. Here we introduce a machine-learning scheme with local-environment-based graphs and graph convolutional neural networks to predict charge density on grid points from the crystal structure. We show the accuracy of this scheme through a comparison of predicted charge densities as well as properties derived from the charge density, and that the scaling is O(N). More importantly, the transferability is shown to be high with respect to different compositions and structures, which results from the explicit encoding of geometry.<br />United States. Department of Energy. Office of Science (Contract DE-AC02-05CH11231)<br />National Science Foundation (U.S.) (Grant ACI-1053575)

Details

Database :
OAIster
Journal :
APS
Notes :
application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1286404652
Document Type :
Electronic Resource