ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction

TL;DR

This paper introduces ADA-GNN, a graph neural network that incorporates both bond distances and angles for crystal property prediction, achieving improved accuracy and efficiency over existing methods.

Contribution

The paper proposes a dual scale neighbor partitioning mechanism and a novel ADA-GNN architecture that separately processes node and structural information for better prediction performance.

Findings

Achieves state-of-the-art results on large-scale datasets.

Improves inference time by efficiently modeling bond angles.

Enhances prediction accuracy with dual scale structural information.

Abstract

Property prediction is a fundamental task in crystal material research. To model atoms and structures, structures represented as graphs are widely used and graph learning-based methods have achieved significant progress. Bond angles and bond distances are two key structural information that greatly influence crystal properties. However, most of the existing works only consider bond distances and overlook bond angles. The main challenge lies in the time cost of handling bond angles, which leads to a significant increase in inference time. To solve this issue, we first propose a crystal structure modeling based on dual scale neighbor partitioning mechanism, which uses a larger scale cutoff for edge neighbors and a smaller scale cutoff for angle neighbors. Then, we propose a novel Atom-Distance-Angle Graph Neural Network (ADA-GNN) for property prediction tasks, which can process node information and structural information separately. The accuracy of predictions and inference time are improved with the dual scale modeling and the specially designed architecture of ADA-GNN. The experimental results validate that our approach achieves state-of-the-art results in two large-scale material benchmark datasets on property prediction tasks.