Exploration for the physical origin and impact of chemical short-range order in high-entropy alloys: Machine learning-assisted study
Atomic-level chemical short-range order (CSRO) in high-entropy alloys (HEAs) has ever garnered increasing attention. However, the mechanisms underlying the effects of CSRO remain poorly understood. Material informatics, through a machine learning (ML) algorithm, can fit the high-dimensional correlat...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127525003120 |
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| author | Panhua Shi Zhen Xie Jiaxuan Si Jianqiao Yu Xiaoyong Wu Yaojun Li Qiu Xu Yuexia Wang |
| author_facet | Panhua Shi Zhen Xie Jiaxuan Si Jianqiao Yu Xiaoyong Wu Yaojun Li Qiu Xu Yuexia Wang |
| author_sort | Panhua Shi |
| collection | DOAJ |
| description | Atomic-level chemical short-range order (CSRO) in high-entropy alloys (HEAs) has ever garnered increasing attention. However, the mechanisms underlying the effects of CSRO remain poorly understood. Material informatics, through a machine learning (ML) algorithm, can fit the high-dimensional correlation between features well and provide an approach for elucidating complex mechanisms. In this study, we introduced a set of interpretable ML workflows and determined the best algorithm (kernel ridge regression (KRR)) for predicting the atomic stress in HEAs, which can deepen the understanding of the formation mechanism of CSRO. Based on first-principles calculations and Monte Carlo methods, we obtained information on each atom at the atomic and electronic levels to establish the ML features. By systematically studying these features, we found that Shapley additive algorithm indicated that t2g orbitals are fundamental factors that dominate atomic stress, which is critical in the CSRO landscape. Additionally, we discovered that the elemental t2g-eg orbital relationship in FeCoNiTi system greatly influences the characteristics of atomic coordination. Moreover, the closely packed configuration efficiently promotes the ideal strength of the short-range order (SRO) HEA compared to its fully random counterpart. We posit that this endeavor provides a theoretical bedrock for grappling with experimental quandaries and theoretical conundrums. |
| format | Article |
| id | doaj-art-cd804c2abdb54982b185965e146530ee |
| institution | OA Journals |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-cd804c2abdb54982b185965e146530ee2025-08-20T02:17:33ZengElsevierMaterials & Design0264-12752025-05-0125311389210.1016/j.matdes.2025.113892Exploration for the physical origin and impact of chemical short-range order in high-entropy alloys: Machine learning-assisted studyPanhua Shi0Zhen Xie1Jiaxuan Si2Jianqiao Yu3Xiaoyong Wu4Yaojun Li5Qiu Xu6Yuexia Wang7Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Institute of Modern Physics, Fudan University, Shanghai 200433, ChinaKey Laboratory of Nuclear Physics and Ion-beam Application (MOE), Institute of Modern Physics, Fudan University, Shanghai 200433, ChinaKey Laboratory of Nuclear Physics and Ion-beam Application (MOE), Institute of Modern Physics, Fudan University, Shanghai 200433, China; The First Sub-Institute, Nuclear Power Institute of China, Chengdu 610005, ChinaKey Laboratory of Nuclear Physics and Ion-beam Application (MOE), Institute of Modern Physics, Fudan University, Shanghai 200433, ChinaThe Fourth Sub-Institute, Nuclear Power Institute of China, Chengdu 610005, ChinaSino-French Institute of Nuclear Engineering and Technology, Sun Yat-sen University, Zhuhai 519082 Guangdong, ChinaInstitute for Integrated Radiation and Nuclear Science, Kyoto University, Osaka 590-0494, JapanKey Laboratory of Nuclear Physics and Ion-beam Application (MOE), Institute of Modern Physics, Fudan University, Shanghai 200433, China; Corresponding author.Atomic-level chemical short-range order (CSRO) in high-entropy alloys (HEAs) has ever garnered increasing attention. However, the mechanisms underlying the effects of CSRO remain poorly understood. Material informatics, through a machine learning (ML) algorithm, can fit the high-dimensional correlation between features well and provide an approach for elucidating complex mechanisms. In this study, we introduced a set of interpretable ML workflows and determined the best algorithm (kernel ridge regression (KRR)) for predicting the atomic stress in HEAs, which can deepen the understanding of the formation mechanism of CSRO. Based on first-principles calculations and Monte Carlo methods, we obtained information on each atom at the atomic and electronic levels to establish the ML features. By systematically studying these features, we found that Shapley additive algorithm indicated that t2g orbitals are fundamental factors that dominate atomic stress, which is critical in the CSRO landscape. Additionally, we discovered that the elemental t2g-eg orbital relationship in FeCoNiTi system greatly influences the characteristics of atomic coordination. Moreover, the closely packed configuration efficiently promotes the ideal strength of the short-range order (SRO) HEA compared to its fully random counterpart. We posit that this endeavor provides a theoretical bedrock for grappling with experimental quandaries and theoretical conundrums.http://www.sciencedirect.com/science/article/pii/S0264127525003120High entropy alloyShort range orderFirst principles calculationsMonte Carlo methodMachine learning |
| spellingShingle | Panhua Shi Zhen Xie Jiaxuan Si Jianqiao Yu Xiaoyong Wu Yaojun Li Qiu Xu Yuexia Wang Exploration for the physical origin and impact of chemical short-range order in high-entropy alloys: Machine learning-assisted study Materials & Design High entropy alloy Short range order First principles calculations Monte Carlo method Machine learning |
| title | Exploration for the physical origin and impact of chemical short-range order in high-entropy alloys: Machine learning-assisted study |
| title_full | Exploration for the physical origin and impact of chemical short-range order in high-entropy alloys: Machine learning-assisted study |
| title_fullStr | Exploration for the physical origin and impact of chemical short-range order in high-entropy alloys: Machine learning-assisted study |
| title_full_unstemmed | Exploration for the physical origin and impact of chemical short-range order in high-entropy alloys: Machine learning-assisted study |
| title_short | Exploration for the physical origin and impact of chemical short-range order in high-entropy alloys: Machine learning-assisted study |
| title_sort | exploration for the physical origin and impact of chemical short range order in high entropy alloys machine learning assisted study |
| topic | High entropy alloy Short range order First principles calculations Monte Carlo method Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S0264127525003120 |
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