Quantum annealing-assisted lattice optimization
Abstract High Entropy Alloys (HEAs) have drawn great interest due to their exceptional properties compared to conventional materials. The configuration of HEA system is considered a key to their superior properties, but exhausting all possible configurations of atom coordinates and species to find t...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-024-01505-1 |
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| _version_ | 1841544408088969216 |
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| author | Zhihao Xu Wenjie Shang Seongmin Kim Eungkyu Lee Tengfei Luo |
| author_facet | Zhihao Xu Wenjie Shang Seongmin Kim Eungkyu Lee Tengfei Luo |
| author_sort | Zhihao Xu |
| collection | DOAJ |
| description | Abstract High Entropy Alloys (HEAs) have drawn great interest due to their exceptional properties compared to conventional materials. The configuration of HEA system is considered a key to their superior properties, but exhausting all possible configurations of atom coordinates and species to find the ground energy state is extremely challenging. In this work, we proposed a quantum annealing-assisted lattice optimization (QALO) algorithm, which is an active learning framework that integrates the Field-aware Factorization Machine (FFM) as the surrogate model for lattice energy prediction, Quantum Annealing (QA) as an optimizer and Machine Learning Potential (MLP) for ground truth energy calculation. By applying our algorithm to the NbMoTaW alloy, we reproduced the Nb depletion and W enrichment observed in bulk HEA. We found our optimized HEAs to have superior mechanical properties compared to the randomly generated alloy configurations. Our algorithm highlights the potential of quantum computing in materials design and discovery, laying a foundation for further exploring and optimizing structure-property relationships. |
| format | Article |
| id | doaj-art-03bccf0e7ff94c229950c347d329edeb |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-03bccf0e7ff94c229950c347d329edeb2025-01-12T12:32:15ZengNature Portfolionpj Computational Materials2057-39602025-01-0111111110.1038/s41524-024-01505-1Quantum annealing-assisted lattice optimizationZhihao Xu0Wenjie Shang1Seongmin Kim2Eungkyu Lee3Tengfei Luo4Department of Aerospace and Mechanical Engineering, University of Notre DameDepartment of Aerospace and Mechanical Engineering, University of Notre DameNational Center for Computational Sciences, Oak Ridge National LaboratoryDepartment of Electronic Engineering, Kyung Hee UniversityDepartment of Aerospace and Mechanical Engineering, University of Notre DameAbstract High Entropy Alloys (HEAs) have drawn great interest due to their exceptional properties compared to conventional materials. The configuration of HEA system is considered a key to their superior properties, but exhausting all possible configurations of atom coordinates and species to find the ground energy state is extremely challenging. In this work, we proposed a quantum annealing-assisted lattice optimization (QALO) algorithm, which is an active learning framework that integrates the Field-aware Factorization Machine (FFM) as the surrogate model for lattice energy prediction, Quantum Annealing (QA) as an optimizer and Machine Learning Potential (MLP) for ground truth energy calculation. By applying our algorithm to the NbMoTaW alloy, we reproduced the Nb depletion and W enrichment observed in bulk HEA. We found our optimized HEAs to have superior mechanical properties compared to the randomly generated alloy configurations. Our algorithm highlights the potential of quantum computing in materials design and discovery, laying a foundation for further exploring and optimizing structure-property relationships.https://doi.org/10.1038/s41524-024-01505-1 |
| spellingShingle | Zhihao Xu Wenjie Shang Seongmin Kim Eungkyu Lee Tengfei Luo Quantum annealing-assisted lattice optimization npj Computational Materials |
| title | Quantum annealing-assisted lattice optimization |
| title_full | Quantum annealing-assisted lattice optimization |
| title_fullStr | Quantum annealing-assisted lattice optimization |
| title_full_unstemmed | Quantum annealing-assisted lattice optimization |
| title_short | Quantum annealing-assisted lattice optimization |
| title_sort | quantum annealing assisted lattice optimization |
| url | https://doi.org/10.1038/s41524-024-01505-1 |
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