Accelerated composition-process-properties design of precipitation-strengthened copper alloys using machine learning based on Bayesian optimization
Designing new alloys with high performance is challenging due to the large search space for composition and process parameters. We propose an alloy design strategy based on machine learning algorithms for navigating the enormous search space. Specifically, feature engineering was applied to screen t...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-02-01
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| Series: | Materials Research Letters |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21663831.2024.2424933 |
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| _version_ | 1849687537535156224 |
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| author | Longjian Li Jinchuan Jie Xiaoyu Guo Gaojie Liu Huijun Kang Zongning Chen Enyu Guo Tongmin Wang |
| author_facet | Longjian Li Jinchuan Jie Xiaoyu Guo Gaojie Liu Huijun Kang Zongning Chen Enyu Guo Tongmin Wang |
| author_sort | Longjian Li |
| collection | DOAJ |
| description | Designing new alloys with high performance is challenging due to the large search space for composition and process parameters. We propose an alloy design strategy based on machine learning algorithms for navigating the enormous search space. Specifically, feature engineering was applied to screen the major features, and a three-step alloy design strategy was employed to extract the required composition. The material design strategy for the multi-performance optimization of Cu-Ni-Si alloy through Bayesian optimization was proposed. This work provides novel insights into the comprehensive properties of Cu-Ni-Si alloys using machine learning with small data, with potential applicability to other materials systems. |
| format | Article |
| id | doaj-art-a7e412ffbdad48969c8cc3121fb9eded |
| institution | DOAJ |
| issn | 2166-3831 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Materials Research Letters |
| spelling | doaj-art-a7e412ffbdad48969c8cc3121fb9eded2025-08-20T03:22:18ZengTaylor & Francis GroupMaterials Research Letters2166-38312025-02-011329410210.1080/21663831.2024.2424933Accelerated composition-process-properties design of precipitation-strengthened copper alloys using machine learning based on Bayesian optimizationLongjian Li0Jinchuan Jie1Xiaoyu Guo2Gaojie Liu3Huijun Kang4Zongning Chen5Enyu Guo6Tongmin Wang7Key Laboratory of Solidification Control and Digital Preparation Technology (Liaoning Province), School of Materials Science and Engineering, Dalian University of Technology, Dalian, People’s Republic of ChinaKey Laboratory of Solidification Control and Digital Preparation Technology (Liaoning Province), School of Materials Science and Engineering, Dalian University of Technology, Dalian, People’s Republic of ChinaKey Laboratory of Solidification Control and Digital Preparation Technology (Liaoning Province), School of Materials Science and Engineering, Dalian University of Technology, Dalian, People’s Republic of ChinaKey Laboratory of Solidification Control and Digital Preparation Technology (Liaoning Province), School of Materials Science and Engineering, Dalian University of Technology, Dalian, People’s Republic of ChinaKey Laboratory of Solidification Control and Digital Preparation Technology (Liaoning Province), School of Materials Science and Engineering, Dalian University of Technology, Dalian, People’s Republic of ChinaKey Laboratory of Solidification Control and Digital Preparation Technology (Liaoning Province), School of Materials Science and Engineering, Dalian University of Technology, Dalian, People’s Republic of ChinaKey Laboratory of Solidification Control and Digital Preparation Technology (Liaoning Province), School of Materials Science and Engineering, Dalian University of Technology, Dalian, People’s Republic of ChinaKey Laboratory of Solidification Control and Digital Preparation Technology (Liaoning Province), School of Materials Science and Engineering, Dalian University of Technology, Dalian, People’s Republic of ChinaDesigning new alloys with high performance is challenging due to the large search space for composition and process parameters. We propose an alloy design strategy based on machine learning algorithms for navigating the enormous search space. Specifically, feature engineering was applied to screen the major features, and a three-step alloy design strategy was employed to extract the required composition. The material design strategy for the multi-performance optimization of Cu-Ni-Si alloy through Bayesian optimization was proposed. This work provides novel insights into the comprehensive properties of Cu-Ni-Si alloys using machine learning with small data, with potential applicability to other materials systems.https://www.tandfonline.com/doi/10.1080/21663831.2024.2424933Machine learningfeature engineering screeningCu-Ni-Si alloymulti-objective optimization |
| spellingShingle | Longjian Li Jinchuan Jie Xiaoyu Guo Gaojie Liu Huijun Kang Zongning Chen Enyu Guo Tongmin Wang Accelerated composition-process-properties design of precipitation-strengthened copper alloys using machine learning based on Bayesian optimization Materials Research Letters Machine learning feature engineering screening Cu-Ni-Si alloy multi-objective optimization |
| title | Accelerated composition-process-properties design of precipitation-strengthened copper alloys using machine learning based on Bayesian optimization |
| title_full | Accelerated composition-process-properties design of precipitation-strengthened copper alloys using machine learning based on Bayesian optimization |
| title_fullStr | Accelerated composition-process-properties design of precipitation-strengthened copper alloys using machine learning based on Bayesian optimization |
| title_full_unstemmed | Accelerated composition-process-properties design of precipitation-strengthened copper alloys using machine learning based on Bayesian optimization |
| title_short | Accelerated composition-process-properties design of precipitation-strengthened copper alloys using machine learning based on Bayesian optimization |
| title_sort | accelerated composition process properties design of precipitation strengthened copper alloys using machine learning based on bayesian optimization |
| topic | Machine learning feature engineering screening Cu-Ni-Si alloy multi-objective optimization |
| url | https://www.tandfonline.com/doi/10.1080/21663831.2024.2424933 |
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