Rapid Assessment of Stable Crystal Structures in Single Phase High Entropy Alloys Via Graph Neural Network Based Surrogate Modelling
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.
Keywords
Cite
@article{arxiv.2409.07664,
title = {Rapid Assessment of Stable Crystal Structures in Single Phase High Entropy Alloys Via Graph Neural Network Based Surrogate Modelling},
author = {Nicholas Beaver and Aniruddha Dive and Marina Wong and Keita Shimanuki and Ananya Patil and Anthony Ferrell and Mohsen B. Kivy},
journal= {arXiv preprint arXiv:2409.07664},
year = {2024}
}
Comments
16 pages, 8 Figures. To be published in Results Materials