Machine learning-assisted design of Ti–V–Nb–Mo refractory high-entropy alloys with higher ductility and specific yield strength
The performance of refractory high-entropy alloys (RHEAs) is closely related to the content of their constituent elements, which makes compositional exploration through traditional trial-and-error methods a challenging and time-consuming endeavour, with the goal of developing an alloy that exhibits...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Journal of Materials Research and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785424030072 |
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| _version_ | 1832595341740867584 |
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| author | Yan Li Junjie Gong Shilong Liang Wei Wu Yongxin Wang Zheng Chen |
| author_facet | Yan Li Junjie Gong Shilong Liang Wei Wu Yongxin Wang Zheng Chen |
| author_sort | Yan Li |
| collection | DOAJ |
| description | The performance of refractory high-entropy alloys (RHEAs) is closely related to the content of their constituent elements, which makes compositional exploration through traditional trial-and-error methods a challenging and time-consuming endeavour, with the goal of developing an alloy that exhibits both high ductility and high specific yield strength. A dataset of the alloys' performance parameters was established by applying first-principles and molecular dynamics calculations. The combination of the aforementioned dataset with the solid solution strengthening (SSH) model and the D (γs/γusf) parameter enabled the construction of a highly accurate strength-ductility prediction model for the alloys through the use of an XGBoost algorithm. The model was employed to predict the compositions of two novel RHEAs and their mechanical properties were verified by experiments. The predicted results are in general agreement with the trends of the experimental data. The Ti35V35Nb10Mo20 alloy exhibiting excellent comprehensive performance, achieving a specific yield strength of 149.55 kPa m3/kg, which is 10.97% higher than that of traditional equiatomic alloy, and a compressive strain exceeding 50%. In conclusion, this work presents an effective alloy design strategy, offering a new approach for the future design of high-performance RHEAs. |
| format | Article |
| id | doaj-art-99463d3b912e4c50a3eb74c0b2fd24b8 |
| institution | Kabale University |
| issn | 2238-7854 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj-art-99463d3b912e4c50a3eb74c0b2fd24b82025-01-19T06:25:48ZengElsevierJournal of Materials Research and Technology2238-78542025-01-013417321743Machine learning-assisted design of Ti–V–Nb–Mo refractory high-entropy alloys with higher ductility and specific yield strengthYan Li0Junjie Gong1Shilong Liang2Wei Wu3Yongxin Wang4Zheng Chen5State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, PR ChinaState Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, PR ChinaState Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, PR ChinaState Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, PR ChinaCorresponding author.; State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, PR ChinaState Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, PR ChinaThe performance of refractory high-entropy alloys (RHEAs) is closely related to the content of their constituent elements, which makes compositional exploration through traditional trial-and-error methods a challenging and time-consuming endeavour, with the goal of developing an alloy that exhibits both high ductility and high specific yield strength. A dataset of the alloys' performance parameters was established by applying first-principles and molecular dynamics calculations. The combination of the aforementioned dataset with the solid solution strengthening (SSH) model and the D (γs/γusf) parameter enabled the construction of a highly accurate strength-ductility prediction model for the alloys through the use of an XGBoost algorithm. The model was employed to predict the compositions of two novel RHEAs and their mechanical properties were verified by experiments. The predicted results are in general agreement with the trends of the experimental data. The Ti35V35Nb10Mo20 alloy exhibiting excellent comprehensive performance, achieving a specific yield strength of 149.55 kPa m3/kg, which is 10.97% higher than that of traditional equiatomic alloy, and a compressive strain exceeding 50%. In conclusion, this work presents an effective alloy design strategy, offering a new approach for the future design of high-performance RHEAs.http://www.sciencedirect.com/science/article/pii/S2238785424030072Refractory high-entropy alloyMachine learningDuctilitySpecific yield strengthAlloy design |
| spellingShingle | Yan Li Junjie Gong Shilong Liang Wei Wu Yongxin Wang Zheng Chen Machine learning-assisted design of Ti–V–Nb–Mo refractory high-entropy alloys with higher ductility and specific yield strength Journal of Materials Research and Technology Refractory high-entropy alloy Machine learning Ductility Specific yield strength Alloy design |
| title | Machine learning-assisted design of Ti–V–Nb–Mo refractory high-entropy alloys with higher ductility and specific yield strength |
| title_full | Machine learning-assisted design of Ti–V–Nb–Mo refractory high-entropy alloys with higher ductility and specific yield strength |
| title_fullStr | Machine learning-assisted design of Ti–V–Nb–Mo refractory high-entropy alloys with higher ductility and specific yield strength |
| title_full_unstemmed | Machine learning-assisted design of Ti–V–Nb–Mo refractory high-entropy alloys with higher ductility and specific yield strength |
| title_short | Machine learning-assisted design of Ti–V–Nb–Mo refractory high-entropy alloys with higher ductility and specific yield strength |
| title_sort | machine learning assisted design of ti v nb mo refractory high entropy alloys with higher ductility and specific yield strength |
| topic | Refractory high-entropy alloy Machine learning Ductility Specific yield strength Alloy design |
| url | http://www.sciencedirect.com/science/article/pii/S2238785424030072 |
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