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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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Crystals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4352/14/12/1099 |
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| Summary: | 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 on prediction accuracy, including lattice parameters and the number of supercells with unique atomic configurations, 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. |
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| ISSN: | 2073-4352 |