Identifying intrinsic factors for ductile-to-brittle transition temperatures in Fe–Al intermetallics via machine learning
The determination of ductile-to-brittle transition temperatures (DBTT) in intermetallic compounds is crucial for assessing their practical applications. In this study, we investigate the intrinsic factors influencing the DBTT of Fe–Al intermetallic compounds through feature engineering. We developed...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-09-01
|
| Series: | Journal of Materials Research and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785423022627 |
| _version_ | 1797646643607109632 |
|---|---|
| author | Dexin Zhu Kunming Pan Hong-Hui Wu Yuan Wu Jie Xiong Xu-Sheng Yang Yongpeng Ren Hua Yu Shizhong Wei Turab Lookman |
| author_facet | Dexin Zhu Kunming Pan Hong-Hui Wu Yuan Wu Jie Xiong Xu-Sheng Yang Yongpeng Ren Hua Yu Shizhong Wei Turab Lookman |
| author_sort | Dexin Zhu |
| collection | DOAJ |
| description | The determination of ductile-to-brittle transition temperatures (DBTT) in intermetallic compounds is crucial for assessing their practical applications. In this study, we investigate the intrinsic factors influencing the DBTT of Fe–Al intermetallic compounds through feature engineering. We developed and evaluated two machine learning strategies for this task. Comparing the strategy that incorporates all features, including alloy compositions and atomic features, with the strategy utilizing selected features, it is found that the latter demonstrates superior computational efficiency and reduces overfitting. Specifically, surrogate models based on two selected features, namely cohesive energy and ionization energy, enable accurate prediction of the DBTT of Fe–Al intermetallics, achieving an accuracy of 95%. Additionally, through symbolic regression, we derived a functional expression that captures the relationship between variations in the DBTT and the selected features of intermetallic compounds. These findings have the potential to serve as a valuable guide for optimizing intermetallic compounds. |
| first_indexed | 2024-03-11T15:04:38Z |
| format | Article |
| id | doaj.art-502bd8fb81bd4482a2c9c2de621833de |
| institution | Directory Open Access Journal |
| issn | 2238-7854 |
| language | English |
| last_indexed | 2024-03-11T15:04:38Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj.art-502bd8fb81bd4482a2c9c2de621833de2023-10-30T06:04:43ZengElsevierJournal of Materials Research and Technology2238-78542023-09-012688368845Identifying intrinsic factors for ductile-to-brittle transition temperatures in Fe–Al intermetallics via machine learningDexin Zhu0Kunming Pan1Hong-Hui Wu2Yuan Wu3Jie Xiong4Xu-Sheng Yang5Yongpeng Ren6Hua Yu7Shizhong Wei8Turab Lookman9National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials, Henan Key Laboratory of High-temperature Structural and Functional Materials, Henan University of Science and Technology, Luoyang, Henan, 471003, ChinaNational Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials, Henan Key Laboratory of High-temperature Structural and Functional Materials, Henan University of Science and Technology, Luoyang, Henan, 471003, China; Longmen Laboratory, Luoyang, Henan, 471003, China; Corresponding author. Longmen Laboratory, Luoyang, Henan, 471003, ChinaState Key Laboratory for Advanced Metals and Materials, University of Science and Technology, Beijing 100083, ChinaState Key Laboratory for Advanced Metals and Materials, University of Science and Technology, Beijing 100083, ChinaMaterials Genome Institute, Shanghai University, Shanghai, 200444, ChinaDepartment of Industrial and Systems Engineering, Research Institute for Advanced Manufacturing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China; Corresponding author.National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials, Henan Key Laboratory of High-temperature Structural and Functional Materials, Henan University of Science and Technology, Luoyang, Henan, 471003, ChinaNational Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials, Henan Key Laboratory of High-temperature Structural and Functional Materials, Henan University of Science and Technology, Luoyang, Henan, 471003, ChinaNational Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials, Henan Key Laboratory of High-temperature Structural and Functional Materials, Henan University of Science and Technology, Luoyang, Henan, 471003, ChinaAiMaterials Research LLC, Santa Fe, NM 87501, United States; Corresponding author.The determination of ductile-to-brittle transition temperatures (DBTT) in intermetallic compounds is crucial for assessing their practical applications. In this study, we investigate the intrinsic factors influencing the DBTT of Fe–Al intermetallic compounds through feature engineering. We developed and evaluated two machine learning strategies for this task. Comparing the strategy that incorporates all features, including alloy compositions and atomic features, with the strategy utilizing selected features, it is found that the latter demonstrates superior computational efficiency and reduces overfitting. Specifically, surrogate models based on two selected features, namely cohesive energy and ionization energy, enable accurate prediction of the DBTT of Fe–Al intermetallics, achieving an accuracy of 95%. Additionally, through symbolic regression, we derived a functional expression that captures the relationship between variations in the DBTT and the selected features of intermetallic compounds. These findings have the potential to serve as a valuable guide for optimizing intermetallic compounds.http://www.sciencedirect.com/science/article/pii/S2238785423022627Ductile-to-brittle transitionIntermetallic compoundsMachine learningSymbolic regression |
| spellingShingle | Dexin Zhu Kunming Pan Hong-Hui Wu Yuan Wu Jie Xiong Xu-Sheng Yang Yongpeng Ren Hua Yu Shizhong Wei Turab Lookman Identifying intrinsic factors for ductile-to-brittle transition temperatures in Fe–Al intermetallics via machine learning Journal of Materials Research and Technology Ductile-to-brittle transition Intermetallic compounds Machine learning Symbolic regression |
| title | Identifying intrinsic factors for ductile-to-brittle transition temperatures in Fe–Al intermetallics via machine learning |
| title_full | Identifying intrinsic factors for ductile-to-brittle transition temperatures in Fe–Al intermetallics via machine learning |
| title_fullStr | Identifying intrinsic factors for ductile-to-brittle transition temperatures in Fe–Al intermetallics via machine learning |
| title_full_unstemmed | Identifying intrinsic factors for ductile-to-brittle transition temperatures in Fe–Al intermetallics via machine learning |
| title_short | Identifying intrinsic factors for ductile-to-brittle transition temperatures in Fe–Al intermetallics via machine learning |
| title_sort | identifying intrinsic factors for ductile to brittle transition temperatures in fe al intermetallics via machine learning |
| topic | Ductile-to-brittle transition Intermetallic compounds Machine learning Symbolic regression |
| url | http://www.sciencedirect.com/science/article/pii/S2238785423022627 |
| work_keys_str_mv | AT dexinzhu identifyingintrinsicfactorsforductiletobrittletransitiontemperaturesinfealintermetallicsviamachinelearning AT kunmingpan identifyingintrinsicfactorsforductiletobrittletransitiontemperaturesinfealintermetallicsviamachinelearning AT honghuiwu identifyingintrinsicfactorsforductiletobrittletransitiontemperaturesinfealintermetallicsviamachinelearning AT yuanwu identifyingintrinsicfactorsforductiletobrittletransitiontemperaturesinfealintermetallicsviamachinelearning AT jiexiong identifyingintrinsicfactorsforductiletobrittletransitiontemperaturesinfealintermetallicsviamachinelearning AT xushengyang identifyingintrinsicfactorsforductiletobrittletransitiontemperaturesinfealintermetallicsviamachinelearning AT yongpengren identifyingintrinsicfactorsforductiletobrittletransitiontemperaturesinfealintermetallicsviamachinelearning AT huayu identifyingintrinsicfactorsforductiletobrittletransitiontemperaturesinfealintermetallicsviamachinelearning AT shizhongwei identifyingintrinsicfactorsforductiletobrittletransitiontemperaturesinfealintermetallicsviamachinelearning AT turablookman identifyingintrinsicfactorsforductiletobrittletransitiontemperaturesinfealintermetallicsviamachinelearning |