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Global Attention based Graph Convolutional Neural Networks for Improved Materials Property Prediction

Authors :
Louis, Steph-Yves
Zhao, Yong
Nasiri, Alireza
Wong, Xiran
Song, Yuqi
Liu, Fei
Hu, Jianjun
Source :
Phys. Chem. Chem. Phys., 2020
Publication Year :
2020

Abstract

Machine learning (ML) methods have gained increasing popularity in exploring and developing new materials. More specifically, graph neural network (GNN) has been applied in predicting material properties. In this work, we develop a novel model, GATGNN, for predicting inorganic material properties based on graph neural networks composed of multiple graph-attention layers (GAT) and a global attention layer. Through the application of the GAT layers, our model can efficiently learn the complex bonds shared among the atoms within each atom's local neighborhood. Subsequently, the global attention layer provides the weight coefficients of each atom in the inorganic crystal material which are used to considerably improve our model's performance. Notably, with the development of our GATGNN model, we show that our method is able to both outperform the previous models' predictions and provide insight into the crystallization of the material.<br />Comment: 11 pages. 5 figures

Details

Database :
arXiv
Journal :
Phys. Chem. Chem. Phys., 2020
Publication Type :
Report
Accession number :
edsarx.2003.13379
Document Type :
Working Paper
Full Text :
https://doi.org/10.1039/D0CP01474E