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 |
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MDPI AG
2024-12-01
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| Series: | Crystals |
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| Online Access: | https://www.mdpi.com/2073-4352/14/12/1099 |
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| author | Nicholas Beaver Aniruddha Dive Marina Wong Keita Shimanuki Ananya Patil Anthony Ferrell Mohsen B. Kivy |
| author_facet | Nicholas Beaver Aniruddha Dive Marina Wong Keita Shimanuki Ananya Patil Anthony Ferrell Mohsen B. Kivy |
| author_sort | Nicholas Beaver |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ba786e19d36b4a8999bbd0c8a07cafe8 |
| institution | Kabale University |
| issn | 2073-4352 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Crystals |
| spelling | doaj-art-ba786e19d36b4a8999bbd0c8a07cafe82024-12-27T14:19:53ZengMDPI AGCrystals2073-43522024-12-011412109910.3390/cryst14121099Rapid Assessment of Stable Crystal Structures in Single-Phase High-Entropy Alloys via Graph Neural Network-Based Surrogate ModellingNicholas Beaver0Aniruddha Dive1Marina Wong2Keita Shimanuki3Ananya Patil4Anthony Ferrell5Mohsen B. Kivy6Materials Engineering Department, California Polytechnic State University, 1 Grand Ave, San Luis Obispo, CA 93407, USAMaterials Engineering Department, California Polytechnic State University, 1 Grand Ave, San Luis Obispo, CA 93407, USAMaterials Engineering Department, California Polytechnic State University, 1 Grand Ave, San Luis Obispo, CA 93407, USAMaterials Engineering Department, California Polytechnic State University, 1 Grand Ave, San Luis Obispo, CA 93407, USAMaterials Engineering Department, California Polytechnic State University, 1 Grand Ave, San Luis Obispo, CA 93407, USAMaterials Engineering Department, California Polytechnic State University, 1 Grand Ave, San Luis Obispo, CA 93407, USAMaterials Engineering Department, California Polytechnic State University, 1 Grand Ave, San Luis Obispo, CA 93407, USATo 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.https://www.mdpi.com/2073-4352/14/12/1099graph neural networkhigh-entropy alloydensity functional theorycrystal structure prediction |
| spellingShingle | Nicholas Beaver Aniruddha Dive Marina Wong Keita Shimanuki Ananya Patil Anthony Ferrell Mohsen B. Kivy Rapid Assessment of Stable Crystal Structures in Single-Phase High-Entropy Alloys via Graph Neural Network-Based Surrogate Modelling Crystals graph neural network high-entropy alloy density functional theory crystal structure prediction |
| title | Rapid Assessment of Stable Crystal Structures in Single-Phase High-Entropy Alloys via Graph Neural Network-Based Surrogate Modelling |
| title_full | Rapid Assessment of Stable Crystal Structures in Single-Phase High-Entropy Alloys via Graph Neural Network-Based Surrogate Modelling |
| title_fullStr | Rapid Assessment of Stable Crystal Structures in Single-Phase High-Entropy Alloys via Graph Neural Network-Based Surrogate Modelling |
| title_full_unstemmed | Rapid Assessment of Stable Crystal Structures in Single-Phase High-Entropy Alloys via Graph Neural Network-Based Surrogate Modelling |
| title_short | Rapid Assessment of Stable Crystal Structures in Single-Phase High-Entropy Alloys via Graph Neural Network-Based Surrogate Modelling |
| title_sort | rapid assessment of stable crystal structures in single phase high entropy alloys via graph neural network based surrogate modelling |
| topic | graph neural network high-entropy alloy density functional theory crystal structure prediction |
| url | https://www.mdpi.com/2073-4352/14/12/1099 |
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