  Graph neural networks (GNN) have been shown to provide substantial
performance improvements for atomistic material representation and modeling
compared with descriptor-based machine learning models. While most existing GNN
models for atomistic predictions are based on atomic distance information, they
do not explicitly incorporate bond angles, which are critical for
distinguishing many atomic structures. Furthermore, many material properties
are known to be sensitive to slight changes in bond angles. We present an
Atomistic Line Graph Neural Network (ALIGNN), a GNN architecture that performs
message passing on both the interatomic bond graph and its line graph
corresponding to bond angles. We demonstrate that angle information can be
explicitly and efficiently included, leading to improved performance on
multiple atomistic prediction tasks. We ALIGNN models for predicting 52
solid-state and molecular properties available in the JARVIS-DFT, Materials
project, and QM9 databases. ALIGNN can outperform some previously reported GNN
models on atomistic prediction tasks by up to 85% in accuracy with better or
comparable model training speed.
