ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material
  Property Prediction
Jiao Huang Qianli Xing Jinglong Ji Bo Yang
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.