Frontiers of Data and Computing ›› 2024, Vol. 6 ›› Issue (1): 79-93.

CSTR: 32002.14.jfdc.CN10-1649/TP.2024.01.008

doi: 10.11871/jfdc.issn.2096-742X.2024.01.008

• Technology and Application • Previous Articles     Next Articles

Progress in application of Graph Convolutional Neural Network in Crystal Material Development

LAO Sisi1,2(),TIAN Ziqi2,*()   

  1. 1. Ningbo University, Ningbo, Zhejiang 315211, China
    2. Ningbo Institute of Materials Technology & Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang 315201, China
  • Received:2022-09-22 Online:2024-02-20 Published:2024-02-21

Abstract:

[Objective] This paper discusses the application of graph convolutional neural network models in crystal materials research and its possible future development direction. [Methods] Firstly, the development and research status of graph convolutional networks are introduced. Secondly, the current application of graph convolutional networks in crystal materials is classified and discussed. Finally, the future research direction of graph convolutional neural networks in crystal materials is discussed. [Results] Graph convolutional neural network has been more and more widely used in natural science research and has shown good results in the structural design and performance prediction of crystal materials. [Conclusions] Graph convolutional neural network has been gradually applied to the design and prediction of crystal materials. Improving the generalization of models and extracting deep features are the future research direction of graph convolutional neural network.

Key words: graph convolutional neural network, crystal material, material design, performance prediction