| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Thermodynamics and Kinetics
|
| Presentation Title |
ALIGNN:Atomistic Line Graph Neural Network for Improved Materials Property Predictions |
| Author(s) |
Kamal Choudhary |
| On-Site Speaker (Planned) |
Kamal Choudhary |
| Abstract Scope |
Graph neural networks (GNN) have been shown to provide much improved performance for representing and modeling atomistic materials 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 develop Atomistic Line Graph Neural Network (ALIGNN) using 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 for materials to provide much improved performance. We train 44 models for predicting several solid-state material properties available in the JARVIS-DFT and materials-project databases. ALIGNN can outperform some of the previously known GNN models by up to 43.8 %. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Modeling and Simulation, Machine Learning |