Technology

ALIGNN

ALIGNN is a deep learning framework that models both bond distances and angles to predict material properties with high precision.

Developed by researchers at NIST, the Atomistic Line Graph Neural Network (ALIGNN) solves a major limitation in materials modeling: capturing the exact geometry of atomic structures (1.2.2). By using a dual-graph architecture, the model alternates message passing between an interatomic bond graph and its corresponding line graph (1.2.3). This allows ALIGNN to explicitly track both two-body (bond length) and three-body (bond angle) interactions (1.1.2). The framework outperforms traditional graph neural networks by up to 85% in accuracy, making it a highly reliable tool for predicting over 100 physical properties (including band gaps and elastic moduli) across 89 elements (1.2.7, 1.2.8).