Quantum and complex-valued hybrid networks for multi-principal element alloys phase prediction
Summary: This study introduces a hybrid network model for phase classification, integrating quantum networks and complex-valued neural networks. This architecture uses elemental composition as its only input, eliminating complex feature engineering. Parameterized quantum networks handle sparse eleme...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004224028098 |
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| author | Shaochun Li Yutong Sun Lu Xiao Weimin Long Gang Wang Junzhi Cui Jingli Ren |
| author_facet | Shaochun Li Yutong Sun Lu Xiao Weimin Long Gang Wang Junzhi Cui Jingli Ren |
| author_sort | Shaochun Li |
| collection | DOAJ |
| description | Summary: This study introduces a hybrid network model for phase classification, integrating quantum networks and complex-valued neural networks. This architecture uses elemental composition as its only input, eliminating complex feature engineering. Parameterized quantum networks handle sparse elemental data and convert data from real to complex domains, increasing information dimensionality. Complex-valued neural networks process data in the complex domain, significantly reducing information loss during transitions. The experimental results show that the hybrid model achieves a phase classification accuracy of 94.93%, outperforming the best machine learning model by 2.27% and the quantum model by 8.67%. Precision, recall, and F1-score are also excellent at 0.9494, 0.9493, and 0.9500, respectively. Additional tests on phase transitions in AlxCoCrFeNi alloys confirm the model’s robust generalization, identifying transition thresholds at 0.46 and 0.88, closely matching the 0.45 and 0.88 reported in related studies. |
| format | Article |
| id | doaj-art-6eb25fbe2a294592904cef2ebcc643f5 |
| institution | DOAJ |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-6eb25fbe2a294592904cef2ebcc643f52025-08-20T02:39:38ZengElsevieriScience2589-00422025-01-0128111158210.1016/j.isci.2024.111582Quantum and complex-valued hybrid networks for multi-principal element alloys phase predictionShaochun Li0Yutong Sun1Lu Xiao2Weimin Long3Gang Wang4Junzhi Cui5Jingli Ren6School of Mathematics and Statistics, Zhengzhou University, Zhengzhou 450001, ChinaInstitute of Systems Science, Chinese Academy of Sciences, Beijing 100190, ChinaSchool of Mathematics and Statistics, Zhengzhou University, Zhengzhou 450001, ChinaSKL of Advanced Brazing Metals & Technology, Zhengzhou Research Institute of Mechanical Engineering, Zhengzhou 450001, ChinaInstitute of Materials, Shanghai University, Shanghai 200444, ChinaInstitute of Computational Mathematics, Chinese Academy of Sciences, Beijing 100190, ChinaSchool of Mathematics and Statistics, Zhengzhou University, Zhengzhou 450001, China; Corresponding authorSummary: This study introduces a hybrid network model for phase classification, integrating quantum networks and complex-valued neural networks. This architecture uses elemental composition as its only input, eliminating complex feature engineering. Parameterized quantum networks handle sparse elemental data and convert data from real to complex domains, increasing information dimensionality. Complex-valued neural networks process data in the complex domain, significantly reducing information loss during transitions. The experimental results show that the hybrid model achieves a phase classification accuracy of 94.93%, outperforming the best machine learning model by 2.27% and the quantum model by 8.67%. Precision, recall, and F1-score are also excellent at 0.9494, 0.9493, and 0.9500, respectively. Additional tests on phase transitions in AlxCoCrFeNi alloys confirm the model’s robust generalization, identifying transition thresholds at 0.46 and 0.88, closely matching the 0.45 and 0.88 reported in related studies.http://www.sciencedirect.com/science/article/pii/S2589004224028098Artificial intelligenceMaterials scienceMaterials classAlloys |
| spellingShingle | Shaochun Li Yutong Sun Lu Xiao Weimin Long Gang Wang Junzhi Cui Jingli Ren Quantum and complex-valued hybrid networks for multi-principal element alloys phase prediction iScience Artificial intelligence Materials science Materials class Alloys |
| title | Quantum and complex-valued hybrid networks for multi-principal element alloys phase prediction |
| title_full | Quantum and complex-valued hybrid networks for multi-principal element alloys phase prediction |
| title_fullStr | Quantum and complex-valued hybrid networks for multi-principal element alloys phase prediction |
| title_full_unstemmed | Quantum and complex-valued hybrid networks for multi-principal element alloys phase prediction |
| title_short | Quantum and complex-valued hybrid networks for multi-principal element alloys phase prediction |
| title_sort | quantum and complex valued hybrid networks for multi principal element alloys phase prediction |
| topic | Artificial intelligence Materials science Materials class Alloys |
| url | http://www.sciencedirect.com/science/article/pii/S2589004224028098 |
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