Study on Prediction of Crystal Properties Based on Deep Learning

doi:10.16182/j.issn1004731x.joss.21-FZ0772

Abstract

Abstract: Predicting crystal properties using traditional machine learning methods requires complex feature engineering. In order to bypass time-consuming feature engineering, element network (ElemNet), representation learning from stoichiometry (Roost), compositionally-restricted attention-based network (CrabNet) and crystal graph convolution neural network (CGCNN) based on deep learning technology are used to simulate the formation energy, total energy per atom, band gap, and Fermi energy of crystal. The residual learning is introduced into CGCNN, and a crystal graph convolution residual neural network (CGCRN) is proposed. In the CGCRN, the number of hidden layers and the number of nodes in the hidden layers are increased, and the hidden layers are connected by residual connection. Meanwhile, the BatchNorm layers are added for normalization. Compared with CGCNN, the prediction accuracy of 4 physical quantities using CGCRN has increased by 1.3%~4.8%, which is conducive to rapid and accurate prediction of ideal crystal materials.

Key words: deep learning, crystal graph convolution, residual learning, crystal properties

CLC Number:

TP183
TP391

Wang Buwei, Wang Min, Fan Qian, Wang Ya'nan, zhang hanwen, Yue Yunliang. Study on Prediction of Crystal Properties Based on Deep Learning[J]. Journal of System Simulation, 2021, 33(12): 2854-2863.

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