Exploration of ductility for refractory high entropy alloys via interpretive machine learning
Refractory high-entropy alloys (RHEAs) having excellent thermo-mechanical properties always suffer from room temperature brittleness, due to the inherent brittleness of refractory metal elements. RHEAs with compressive ductility ≥30 % hold particular commercial value. While ductility can be engineer...
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Elsevier
2025-07-01
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| Series: | Journal of Materials Research and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785425015078 |
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| author | Shaolong Zheng Lingwei Yang Liyang Fang Chenran Xu Guanglong Xu Yifang Ouyang Xiaoma Tao |
| author_facet | Shaolong Zheng Lingwei Yang Liyang Fang Chenran Xu Guanglong Xu Yifang Ouyang Xiaoma Tao |
| author_sort | Shaolong Zheng |
| collection | DOAJ |
| description | Refractory high-entropy alloys (RHEAs) having excellent thermo-mechanical properties always suffer from room temperature brittleness, due to the inherent brittleness of refractory metal elements. RHEAs with compressive ductility ≥30 % hold particular commercial value. While ductility can be engineered through alloying composition and processing condition optimization, this remains challenging due to the virtually infinite and vast unexplored compositional space. Data-driven machine learning (ML) can significantly reduce experimental costs and time by establishing robust correlations between RHEA compositions and ductility. This study constructs an ML model for accurate ductility prediction from sparse compositional data, accelerating the design of ductile RHEAs within infinite compositional space. Two ML algorithms, decision tree (DT) and CatBoost, are trained using physical parameters, with CatBoost demonstrating superior performance in RHEA ductility classification. Integration of the shapley additive explanations (SHAP) model with sample data reveals key relationships between physical parameters and RHEA ductility, providing theoretical insights for developing high-ductility RHEAs. The analysis identifies δχ, Tm, VEC, and ΔHmix as critical factors in RHEA ductility prediction, with a quasi-linear relationship observed between ΔHmix and ductility. Through the prediction and subsequent preparation of TiZrNbTax (x=0.3, 0.5, 0.7, 1) alloys, the experimental results confirmed the reliability of the proposed model and key parameters. This approach establishes a novel methodology for exploring and developing RHEAs with excellent ductility. |
| format | Article |
| id | doaj-art-ea768e342a164914979a1d50812bebae |
| institution | OA Journals |
| issn | 2238-7854 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj-art-ea768e342a164914979a1d50812bebae2025-08-20T02:07:46ZengElsevierJournal of Materials Research and Technology2238-78542025-07-01371243125610.1016/j.jmrt.2025.06.085Exploration of ductility for refractory high entropy alloys via interpretive machine learningShaolong Zheng0Lingwei Yang1Liyang Fang2Chenran Xu3Guanglong Xu4Yifang Ouyang5Xiaoma Tao6School of Physical Science and Technology, Guangxi Key Laboratory for Relativistic Astrophysics, State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures, Guangxi University, Nanning, 530004, ChinaHypervelocity Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang, 621000, China; National Key Laboratory of Aerospace Physics in Fluids, China Aerodynamics Research and Development Center, Mianyang, 621000, China; Corresponding author. Hypervelocity Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang, 621000, China.School of Physical Science and Technology, Guangxi Key Laboratory for Relativistic Astrophysics, State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures, Guangxi University, Nanning, 530004, ChinaSchool of Physical Science and Technology, Guangxi Key Laboratory for Relativistic Astrophysics, State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures, Guangxi University, Nanning, 530004, ChinaTech Institute of Advanced Materials & College of Materials Science and Engineering, Nanjing Tech University, Nanjing, 211816, ChinaSchool of Physical Science and Technology, Guangxi Key Laboratory for Relativistic Astrophysics, State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures, Guangxi University, Nanning, 530004, ChinaSchool of Physical Science and Technology, Guangxi Key Laboratory for Relativistic Astrophysics, State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures, Guangxi University, Nanning, 530004, China; Corresponding author.Refractory high-entropy alloys (RHEAs) having excellent thermo-mechanical properties always suffer from room temperature brittleness, due to the inherent brittleness of refractory metal elements. RHEAs with compressive ductility ≥30 % hold particular commercial value. While ductility can be engineered through alloying composition and processing condition optimization, this remains challenging due to the virtually infinite and vast unexplored compositional space. Data-driven machine learning (ML) can significantly reduce experimental costs and time by establishing robust correlations between RHEA compositions and ductility. This study constructs an ML model for accurate ductility prediction from sparse compositional data, accelerating the design of ductile RHEAs within infinite compositional space. Two ML algorithms, decision tree (DT) and CatBoost, are trained using physical parameters, with CatBoost demonstrating superior performance in RHEA ductility classification. Integration of the shapley additive explanations (SHAP) model with sample data reveals key relationships between physical parameters and RHEA ductility, providing theoretical insights for developing high-ductility RHEAs. The analysis identifies δχ, Tm, VEC, and ΔHmix as critical factors in RHEA ductility prediction, with a quasi-linear relationship observed between ΔHmix and ductility. Through the prediction and subsequent preparation of TiZrNbTax (x=0.3, 0.5, 0.7, 1) alloys, the experimental results confirmed the reliability of the proposed model and key parameters. This approach establishes a novel methodology for exploring and developing RHEAs with excellent ductility.http://www.sciencedirect.com/science/article/pii/S2238785425015078Refractory high entropy alloysDuctilityMachine learningClassification |
| spellingShingle | Shaolong Zheng Lingwei Yang Liyang Fang Chenran Xu Guanglong Xu Yifang Ouyang Xiaoma Tao Exploration of ductility for refractory high entropy alloys via interpretive machine learning Journal of Materials Research and Technology Refractory high entropy alloys Ductility Machine learning Classification |
| title | Exploration of ductility for refractory high entropy alloys via interpretive machine learning |
| title_full | Exploration of ductility for refractory high entropy alloys via interpretive machine learning |
| title_fullStr | Exploration of ductility for refractory high entropy alloys via interpretive machine learning |
| title_full_unstemmed | Exploration of ductility for refractory high entropy alloys via interpretive machine learning |
| title_short | Exploration of ductility for refractory high entropy alloys via interpretive machine learning |
| title_sort | exploration of ductility for refractory high entropy alloys via interpretive machine learning |
| topic | Refractory high entropy alloys Ductility Machine learning Classification |
| url | http://www.sciencedirect.com/science/article/pii/S2238785425015078 |
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