About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Now On-Demand Only - Atomistic Line Graph Neural Network for Improved Materials Property Predictions |
Author(s) |
Kamal Choudhary, Brian DeCost |
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 train 55 models for predicting several solid-state and molecular properties available in the JARVIS-DFT, Materials project and QM9 databases. ALIGNN can outperform some of the previously known GNN models by up to 43.8 % in accuracy with model training speed better or comparable to them. |
Proceedings Inclusion? |
Undecided |