Atomistic Line Graph Neural Network for improved materials property predictions

Abstract 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. We ALIGNN models for predicting 52 solid-state and molecular properties available in the JARVIS-DFT, Materials project, and QM9 databases.

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• 1 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.
• 2 Additionally, many material properties are known to be sensitive to slight changes in bond angles.
• 3 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.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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