Do Graph Neural Networks Work for High Entropy Alloys?

TL;DR

This paper introduces LESets, a novel GNN-based model for predicting properties of high-entropy alloys by representing them as local environment graphs, overcoming previous limitations due to lack of long-range order.

Contribution

The paper presents a new local environment graph representation and LESets model, extending GNN applicability to disordered materials like HEAs with complex compositions.

Findings

LESets accurately predicts mechanical properties of quaternary HEAs

The approach provides interpretable insights into HEA design

Extends GNN use to disordered, multi-component materials

Abstract

Graph neural networks (GNNs) have excelled in predictive modeling for both crystals and molecules, owing to the expressiveness of graph representations. High-entropy alloys (HEAs), however, lack chemical long-range order, limiting the applicability of current graph representations. To overcome this challenge, we propose a representation of HEAs as a collection of local environment (LE) graphs. Based on this representation, we introduce the LESets machine learning model, an accurate, interpretable GNN for HEA property prediction. We demonstrate the accuracy of LESets in modeling the mechanical properties of quaternary HEAs. Through analyses and interpretation, we further extract insights into the modeling and design of HEAs. In a broader sense, LESets extends the potential applicability of GNNs to disordered materials with combinatorial complexity formed by diverse constituents and their flexible configurations.