Intelligent prediction and oriented design of high-hardness high-entropy ceramics
High-entropy ceramics are considered vital candidate materials for applications in rail transportation and advanced manufacturing due to their exceptional hardness and wear resistance. However, the intricate relationship between their composition and mechanical properties presents challenges for on-...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Journal of Materials Research and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785425009846 |
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| author | Anzhe Wang Jicheng Liu Linwei Guo Kejie Qu Haishen Xie Yawei Li Bin Du |
| author_facet | Anzhe Wang Jicheng Liu Linwei Guo Kejie Qu Haishen Xie Yawei Li Bin Du |
| author_sort | Anzhe Wang |
| collection | DOAJ |
| description | High-entropy ceramics are considered vital candidate materials for applications in rail transportation and advanced manufacturing due to their exceptional hardness and wear resistance. However, the intricate relationship between their composition and mechanical properties presents challenges for on-demand material design. This work utilizes machine learning and heuristic optimization algorithms to achieve accurate predictions of bulk high-entropy ceramics hardness (with validation set errors <10 %) and the oriented design of high-entropy ceramics with a hardness of 25 GPa (with an average error of 2.6 %). This achievement is attributed to three key innovations: the construction of the feature space based on the Pearson correlation coefficient and genetic algorithm, along with algorithm selection and optimization through hyperparameter tuning; the novel combination of reduced-dimensionality component compositions with atomic/precursor descriptors, achieving a model R2 value of up to 0.898; the optimization of constituent elements using genetic algorithm and principal component analysis, providing direct guidance for the design of high-hardness high-entropy ceramics. Through these methodologies, new high-entropy ceramics compositions, typically represented by (Ti0·25Zr0·25Nb0.25Hf0.25)C, were successfully designed, achieving hardness of up to 23.9–25.2 GPa. This oriented design methodology holds promise for accelerating the on-demand design of high-entropy ceramics. |
| format | Article |
| id | doaj-art-41d965cd6c054681804cec54005cab44 |
| institution | OA Journals |
| issn | 2238-7854 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj-art-41d965cd6c054681804cec54005cab442025-08-20T02:19:19ZengElsevierJournal of Materials Research and Technology2238-78542025-05-01366015602310.1016/j.jmrt.2025.04.163Intelligent prediction and oriented design of high-hardness high-entropy ceramicsAnzhe Wang0Jicheng Liu1Linwei Guo2Kejie Qu3Haishen Xie4Yawei Li5Bin Du6School of Materials Science and Engineering, Nanjing Institute of Technology, Nanjing, 211167, China; Jiangsu Key Laboratory of Advanced Structural Materials and Application Technology, Nanjing Institute of Technology, Nanjing, 211167, China; Corresponding author. School of Materials Science and Engineering, Nanjing Institute of Technology, Nanjing, 211167, China.School of Materials Science and Engineering, Nanjing Institute of Technology, Nanjing, 211167, ChinaSchool of Physics and Materials Science, Guangzhou University, Guangzhou, 510006, ChinaSchool of Materials Science and Engineering, Nanjing Institute of Technology, Nanjing, 211167, ChinaJointech Tooling & Moulding Technology Co., Ltd., Suzhou, 215131, ChinaState Key Laboratory of Advanced Refractories, Wuhan University of Science and Technology, Wuhan, 430081, China; Corresponding author.School of Physics and Materials Science, Guangzhou University, Guangzhou, 510006, China; Corresponding author.High-entropy ceramics are considered vital candidate materials for applications in rail transportation and advanced manufacturing due to their exceptional hardness and wear resistance. However, the intricate relationship between their composition and mechanical properties presents challenges for on-demand material design. This work utilizes machine learning and heuristic optimization algorithms to achieve accurate predictions of bulk high-entropy ceramics hardness (with validation set errors <10 %) and the oriented design of high-entropy ceramics with a hardness of 25 GPa (with an average error of 2.6 %). This achievement is attributed to three key innovations: the construction of the feature space based on the Pearson correlation coefficient and genetic algorithm, along with algorithm selection and optimization through hyperparameter tuning; the novel combination of reduced-dimensionality component compositions with atomic/precursor descriptors, achieving a model R2 value of up to 0.898; the optimization of constituent elements using genetic algorithm and principal component analysis, providing direct guidance for the design of high-hardness high-entropy ceramics. Through these methodologies, new high-entropy ceramics compositions, typically represented by (Ti0·25Zr0·25Nb0.25Hf0.25)C, were successfully designed, achieving hardness of up to 23.9–25.2 GPa. This oriented design methodology holds promise for accelerating the on-demand design of high-entropy ceramics.http://www.sciencedirect.com/science/article/pii/S2238785425009846Machine learningHigh-entropy ceramicsHardnessMechanical properties predictionOriented design |
| spellingShingle | Anzhe Wang Jicheng Liu Linwei Guo Kejie Qu Haishen Xie Yawei Li Bin Du Intelligent prediction and oriented design of high-hardness high-entropy ceramics Journal of Materials Research and Technology Machine learning High-entropy ceramics Hardness Mechanical properties prediction Oriented design |
| title | Intelligent prediction and oriented design of high-hardness high-entropy ceramics |
| title_full | Intelligent prediction and oriented design of high-hardness high-entropy ceramics |
| title_fullStr | Intelligent prediction and oriented design of high-hardness high-entropy ceramics |
| title_full_unstemmed | Intelligent prediction and oriented design of high-hardness high-entropy ceramics |
| title_short | Intelligent prediction and oriented design of high-hardness high-entropy ceramics |
| title_sort | intelligent prediction and oriented design of high hardness high entropy ceramics |
| topic | Machine learning High-entropy ceramics Hardness Mechanical properties prediction Oriented design |
| url | http://www.sciencedirect.com/science/article/pii/S2238785425009846 |
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