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

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

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 comp...

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Bibliographic Details
Main Authors: Nicholas Beaver, Aniruddha Dive, Marina Wong, Keita Shimanuki, Ananya Patil, Anthony Ferrell, Mohsen B. Kivy
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Crystals
Subjects:
graph neural network
high-entropy alloy
density functional theory
crystal structure prediction
Online Access:https://www.mdpi.com/2073-4352/14/12/1099
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