ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction
This work addresses a domain-specific problem in materials science by improving prediction accuracy and inference time for crystal property prediction, though it is incremental as it builds on existing graph-based methods.
The paper tackles the problem of predicting crystal material properties by incorporating both bond distances and bond angles, which are often overlooked due to computational costs, and achieves state-of-the-art results on two large-scale benchmark datasets.
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.