Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery

Abstract

The recently proposed crystal graph convolutional neural network (CGCNN) offers a highly versatile and accurate machine learning (ML) framework by learning material properties directly from graphlike representations of crystal structures (``crystal graphs''). Here, we develop an improved variant of the CGCNN model (iCGCNN) that outperforms the original by incorporating information of the Voronoi tessellated crystal structure, explicit three-body correlations of neighboring constituent atoms, and an optimized chemical representation of interatomic bonds in the crystal graphs. We demonstrate the accuracy of the improved framework in two distinct illustrations: First, when trained/validated on 180 000/20 000 density functional theory (DFT) calculated thermodynamic stability entries taken from the Open Quantum Materials Database (OQMD) and evaluated on a separate test set of 230 000 entries, iCGCNN achieves a predictive accuracy that is significantly improved, i.e., 20% higher than that of the original CGCNN. Second, when used to assist a high-throughput search for materials in the $\mathrm{ThC}{\mathrm{r}}_{2}\mathrm{S}{\mathrm{i}}_{2}$ structure-type, iCGCNN exhibited a success rate of $31%$ which is 155 times higher than an undirected high-throughput search and 2.4 times higher than that of the original CGCNN. Using both CGCNN and iCGCNN, we screened 132 600 compounds with elemental decorations of the $\mathrm{ThC}{\mathrm{r}}_{2}\mathrm{S}{\mathrm{i}}_{2}$ prototype crystal structure and identified a total of 97 unique stable compounds by performing 757 DFT calculations, accelerating the computational time of the high-throughput search by a factor of 65. Our results suggest that the iCGCNN can be used to accelerate high-throughput discoveries of new materials by quickly and accurately identifying crystalline compounds with properties of interest.

Topics & Concepts