Rapid Assessment of Stable Crystal Structures in Single Phase High Entropy Alloys Via Graph Neural Network Based Surrogate Modelling

TL;DR

This paper introduces a graph neural network-based surrogate model for rapid, accurate prediction of stable crystal structures in single-phase high entropy alloys, validated through experiments and comparisons with traditional methods.

Contribution

The study presents a novel ALIGNN-FF approach that significantly accelerates structure prediction in high entropy alloys, outperforming conventional computational techniques.

Findings

Successful prediction of 132 high entropy alloys structures

Validation against density functional theory and experiments

Identification of factors affecting prediction accuracy

Abstract

In an effort to develop a rapid, reliable, and cost-effective method for predicting the structure of single-phase high entropy alloys, a Graph Neural Network (ALIGNN-FF) based approach was introduced. This method was successfully tested on 132 different high entropy alloys, and the results were analyzed and compared with density functional theory and valence electron concentration calculations. Additionally, the effects of various factors, including lattice parameters and the number of supercells with unique atomic configurations, on the prediction accuracy were investigated. The ALIGNN-FF based approach was subsequently used to predict the structure of a novel cobalt-free 3d high entropy alloy, and the result was experimentally verified.