Edge-Aware Graph Attention Model for Structural Optimization of High Entropy Carbides

TL;DR

This paper presents an edge-aware graph attention neural network that efficiently predicts atomic structures of high-entropy carbides, offering a scalable, accurate, and physics-informed alternative to traditional computational methods for materials discovery.

Contribution

The introduction of a physics-informed, edge-aware graph attention model specifically designed for high-entropy materials, improving prediction accuracy and computational efficiency.

Findings

Achieved high accuracy in predicting relaxed structures of high-entropy carbides.

Demonstrated the model's transferability across different compositions.

Showed significant reduction in computational cost compared to DFT methods.

Abstract

Predicting relaxed atomic structures of chemically complex materials remains a major computational challenge, particularly for high-entropy systems where traditional first-principles methods become prohibitively expensive. We introduce the edge-aware graph attention model, a physics-informed graph neural network tailored for predicting relaxed atomic structures of high-entropy systems. the edge-aware graph attention model employs chemically and geometrically informed descriptors that capture both atomic properties and local structural environments. To effectively capture atomic interactions, our model integrates a multi-head self-attention mechanism that adaptively weighs neighbouring atoms using both node and edge features. This edge-aware attention framework learn complex chemical and structural relationships independent of global orientation or position. We trained and evaluated the edge-aware GAT model on a dataset of carbide systems, spanning binary to high-entropy carbide compositions, and demonstrated its accuracy, convergence efficiency, and transferability. The architecture is lightweight, with a very low computational footprint, making it highly suitable for large-scale materials screening. By providing invariance to rigid-body transformations and leveraging domain-informed attention mechanisms, our model delivers a fast, scalable, and cost-effective alternative to DFT, enabling accelerated discovery and screening of entropy-stabilised materials.