Shaolong Zheng , Lingwei Yang , Liyang Fang , Chenran Xu , Guanglong Xu , Yifang Ouyang , Xiaoma Tao
{"title":"Exploration of ductility for refractory high entropy alloys via interpretive machine learning","authors":"Shaolong Zheng , Lingwei Yang , Liyang Fang , Chenran Xu , Guanglong Xu , Yifang Ouyang , Xiaoma Tao","doi":"10.1016/j.jmrt.2025.06.085","DOIUrl":null,"url":null,"abstract":"<div><div>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, <em>T</em><sub><em>m</em></sub>, VEC, and ΔH<sub><em>mix</em></sub> as critical factors in RHEA ductility prediction, with a quasi-linear relationship observed between ΔH<sub><em>mix</em></sub> and ductility. Through the prediction and subsequent preparation of TiZrNbTa<sub>x</sub> (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.</div></div>","PeriodicalId":54332,"journal":{"name":"Journal of Materials Research and Technology-Jmr&t","volume":"37 ","pages":"Pages 1243-1256"},"PeriodicalIF":6.2000,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Materials Research and Technology-Jmr&t","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2238785425015078","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
The Journal of Materials Research and Technology is a publication of ABM - Brazilian Metallurgical, Materials and Mining Association - and publishes four issues per year also with a free version online (www.jmrt.com.br). The journal provides an international medium for the publication of theoretical and experimental studies related to Metallurgy, Materials and Minerals research and technology. Appropriate submissions to the Journal of Materials Research and Technology should include scientific and/or engineering factors which affect processes and products in the Metallurgy, Materials and Mining areas.