Predicting the stacking fault energy in FCC high-entropy alloys based on data-driven machine learning
The properties of high-entropy alloys (HEAs) depend primarily on the composition and content of elements. However, getting the optimal composition of alloying elements through the traditional ''trial and error'' method is challenging, especially for non-equiatomic HEAs with a wid...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Journal of Materials Research and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785423020070 |
| _version_ | 1797646747813543936 |
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| author | Xiaoyang Zhang Ruifeng Dong Qingwei Guo Hua Hou Yuhong Zhao |
| author_facet | Xiaoyang Zhang Ruifeng Dong Qingwei Guo Hua Hou Yuhong Zhao |
| author_sort | Xiaoyang Zhang |
| collection | DOAJ |
| description | The properties of high-entropy alloys (HEAs) depend primarily on the composition and content of elements. However, getting the optimal composition of alloying elements through the traditional ''trial and error'' method is challenging, especially for non-equiatomic HEAs with a wide range of composition space. In this study, based on the knowledge that stacking fault energy (SFE) is the most crucial intrinsic property to determine the deformation mechanism and to optimize the mechanical properties of FCC HEAs, classical machine learning classification models including support vector classification (SVC) and random forest (RF), and deep learning regression model (Back Propagation Neural Network) were established to predict the stacking fault energy of Co–Cr–Fe–Mn–Ni–V–Al high-entropy alloys. These models can obtain the SFE data of any atomic ratio composition of the FCC structured Co–Cr–Fe–Mn–Ni–V–Al high-entropy alloy quickly and accurately. The high accuracy of these models indicates that using the compositions as features to predict stacking fault energy is feasible. Meanwhile, the monotonic relationship between alloying elements and SFE makes it possible to change the SFE of high-entropy alloy by fine-tuning the composition to realize the control of material deformation mechanism and mechanical properties. Component-based machine learning models provide a new method for rapidly discovering high-entropy alloys with exceptional strength and flexibility. |
| first_indexed | 2024-03-11T15:06:15Z |
| format | Article |
| id | doaj.art-d75fd67ae05a4d1d9c800dc6862d147a |
| institution | Directory Open Access Journal |
| issn | 2238-7854 |
| language | English |
| last_indexed | 2024-03-11T15:06:15Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj.art-d75fd67ae05a4d1d9c800dc6862d147a2023-10-30T06:03:47ZengElsevierJournal of Materials Research and Technology2238-78542023-09-012648134824Predicting the stacking fault energy in FCC high-entropy alloys based on data-driven machine learningXiaoyang Zhang0Ruifeng Dong1Qingwei Guo2Hua Hou3Yuhong Zhao4School of Materials Science and Engineering, Collaborative Innovation Center of Ministry of Education and Shanxi Province for High-performance Al/Mg Alloy Materials, North University of China, Taiyuan 030051, PR ChinaSchool of Materials Science and Engineering, Collaborative Innovation Center of Ministry of Education and Shanxi Province for High-performance Al/Mg Alloy Materials, North University of China, Taiyuan 030051, PR ChinaSchool of Materials Science and Engineering, Collaborative Innovation Center of Ministry of Education and Shanxi Province for High-performance Al/Mg Alloy Materials, North University of China, Taiyuan 030051, PR ChinaSchool of Materials Science and Engineering, Collaborative Innovation Center of Ministry of Education and Shanxi Province for High-performance Al/Mg Alloy Materials, North University of China, Taiyuan 030051, PR China; School of Materials Science and Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, PR ChinaSchool of Materials Science and Engineering, Collaborative Innovation Center of Ministry of Education and Shanxi Province for High-performance Al/Mg Alloy Materials, North University of China, Taiyuan 030051, PR China; Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, PR China; Institute of Materials Intelligent Technology, Liaoning Academy of Materials, Shenyang 110004, PR China; Corresponding author. School of Materials Science and Engineering, Collaborative Innovation Center of Ministry of Education and Shanxi Province for High-performance Al/Mg Alloy Materials, North University of China, Taiyuan 030051, PR China.The properties of high-entropy alloys (HEAs) depend primarily on the composition and content of elements. However, getting the optimal composition of alloying elements through the traditional ''trial and error'' method is challenging, especially for non-equiatomic HEAs with a wide range of composition space. In this study, based on the knowledge that stacking fault energy (SFE) is the most crucial intrinsic property to determine the deformation mechanism and to optimize the mechanical properties of FCC HEAs, classical machine learning classification models including support vector classification (SVC) and random forest (RF), and deep learning regression model (Back Propagation Neural Network) were established to predict the stacking fault energy of Co–Cr–Fe–Mn–Ni–V–Al high-entropy alloys. These models can obtain the SFE data of any atomic ratio composition of the FCC structured Co–Cr–Fe–Mn–Ni–V–Al high-entropy alloy quickly and accurately. The high accuracy of these models indicates that using the compositions as features to predict stacking fault energy is feasible. Meanwhile, the monotonic relationship between alloying elements and SFE makes it possible to change the SFE of high-entropy alloy by fine-tuning the composition to realize the control of material deformation mechanism and mechanical properties. Component-based machine learning models provide a new method for rapidly discovering high-entropy alloys with exceptional strength and flexibility.http://www.sciencedirect.com/science/article/pii/S2238785423020070High-entropy alloysStacking fault energyMachine learningAlloy design |
| spellingShingle | Xiaoyang Zhang Ruifeng Dong Qingwei Guo Hua Hou Yuhong Zhao Predicting the stacking fault energy in FCC high-entropy alloys based on data-driven machine learning Journal of Materials Research and Technology High-entropy alloys Stacking fault energy Machine learning Alloy design |
| title | Predicting the stacking fault energy in FCC high-entropy alloys based on data-driven machine learning |
| title_full | Predicting the stacking fault energy in FCC high-entropy alloys based on data-driven machine learning |
| title_fullStr | Predicting the stacking fault energy in FCC high-entropy alloys based on data-driven machine learning |
| title_full_unstemmed | Predicting the stacking fault energy in FCC high-entropy alloys based on data-driven machine learning |
| title_short | Predicting the stacking fault energy in FCC high-entropy alloys based on data-driven machine learning |
| title_sort | predicting the stacking fault energy in fcc high entropy alloys based on data driven machine learning |
| topic | High-entropy alloys Stacking fault energy Machine learning Alloy design |
| url | http://www.sciencedirect.com/science/article/pii/S2238785423020070 |
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