Predicting Material Properties Using a 3D Graph Neural Network with Invariant Local Descriptors

• URL: http://arxiv.org/abs/2102.11023v1
• Date: Tue, 16 Feb 2021 19:56:54 GMT
• Title: Predicting Material Properties Using a 3D Graph Neural Network with Invariant Local Descriptors
• Authors: Boyu Zhang, Mushen Zhou, Jianzhong Wu, Fuchang Gao
• Abstract summary: Accurately predicting material properties is critical for discovering and designing novel materials. Among the machine learning methods, graph convolution neural networks (GCNNs) have been one of the most successful ones. We propose an adaptive GCNN with novel convolutions that model interactions among all neighboring atoms in three-dimensional space simultaneously.
• Score: 0.4956709222278243
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurately predicting material properties is critical for discovering and designing novel materials. Machine learning technologies have attracted significant attention in materials science community for their potential for large-scale screening. Among the machine learning methods, graph convolution neural networks (GCNNs) have been one of the most successful ones because of their flexibility and effectiveness in describing 3D structural data. Most existing GCNN models focus on the topological structure but overly simplify the three-dimensional geometric structure. In materials science, the 3D-spatial distribution of the atoms, however, is crucial for determining the atomic states and interatomic forces. In this paper, we propose an adaptive GCNN with novel convolutions that model interactions among all neighboring atoms in three-dimensional space simultaneously. We apply the model to two distinctly challenging problems on predicting material properties. The first is Henry's constant for gas adsorption in Metal-Organic Frameworks (MOFs), which is notoriously difficult because of its high sensitivity to atomic configurations. The second is the ion conductivity of solid-state crystal materials, which is difficult because of very few labeled data available for training. The new model outperforms existing GCNN models on both data sets, suggesting that some important three-dimensional geometric information is indeed captured by the new model.

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