Machine learning-assisted design of high-entropy alloys with superior mechanical properties
Most recently, high-entropy alloys (HEAs) with 5 or more elements open a new area for materials exploration with substantial mechanical properties. The large composition space and numerous structures of HEAs bring significant difficulties for phase design and determination of mechanical property. Ma...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | Journal of Materials Research and Technology |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785424020192 |
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| author | Jianye He Zezhou Li Pingluo Zhao Hongmei Zhang Fan Zhang Lin Wang Xingwang Cheng |
| author_facet | Jianye He Zezhou Li Pingluo Zhao Hongmei Zhang Fan Zhang Lin Wang Xingwang Cheng |
| author_sort | Jianye He |
| collection | DOAJ |
| description | Most recently, high-entropy alloys (HEAs) with 5 or more elements open a new area for materials exploration with substantial mechanical properties. The large composition space and numerous structures of HEAs bring significant difficulties for phase design and determination of mechanical property. Machine learning, one of most rapidly growing scientific and technical field, meets at the intersection of computer science and materials science, and at the center of artificial intelligence. Machine learning provides the opportunity to build up the relationship between multiple physical properties and mechanical properties. The fast changes of this field call for significant practice for materials community to utilize it as a more efficient, accurate and interpretable tool. In this review, we summarize the most promising machine learning models, combined with high-throughput simulation and experimental screening, to predict and fabricate HEAs with desired superb mechanical properties. |
| format | Article |
| id | doaj-art-2ea2bfc97ec442458897d13f82fe910e |
| institution | DOAJ |
| issn | 2238-7854 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj-art-2ea2bfc97ec442458897d13f82fe910e2025-08-20T02:39:13ZengElsevierJournal of Materials Research and Technology2238-78542024-11-013326028610.1016/j.jmrt.2024.09.014Machine learning-assisted design of high-entropy alloys with superior mechanical propertiesJianye He0Zezhou Li1Pingluo Zhao2Hongmei Zhang3Fan Zhang4Lin Wang5Xingwang Cheng6School of Materials Science and Engineering, Beijing Institute of Technology, Beijing, 100081, China; National Key Laboratory of Science and Technology on Materials Under Shock and Impact, Beijing, 100081, ChinaSchool of Materials Science and Engineering, Beijing Institute of Technology, Beijing, 100081, China; Tangshan Research Institute, Beijing Institute of Technology, Tangshan, 063000, China; National Key Laboratory of Science and Technology on Materials Under Shock and Impact, Beijing, 100081, China; Corresponding author. School of Materials Science and Engineering, Beijing Institute of Technology, Beijing, 100081, China.School of Materials Science and Engineering, Beijing Institute of Technology, Beijing, 100081, China; National Key Laboratory of Science and Technology on Materials Under Shock and Impact, Beijing, 100081, ChinaSchool of Materials Science and Engineering, Beijing Institute of Technology, Beijing, 100081, China; Tangshan Research Institute, Beijing Institute of Technology, Tangshan, 063000, China; National Key Laboratory of Science and Technology on Materials Under Shock and Impact, Beijing, 100081, ChinaSchool of Materials Science and Engineering, Beijing Institute of Technology, Beijing, 100081, China; Tangshan Research Institute, Beijing Institute of Technology, Tangshan, 063000, China; National Key Laboratory of Science and Technology on Materials Under Shock and Impact, Beijing, 100081, ChinaSchool of Materials Science and Engineering, Beijing Institute of Technology, Beijing, 100081, China; National Key Laboratory of Science and Technology on Materials Under Shock and Impact, Beijing, 100081, ChinaSchool of Materials Science and Engineering, Beijing Institute of Technology, Beijing, 100081, China; Tangshan Research Institute, Beijing Institute of Technology, Tangshan, 063000, China; National Key Laboratory of Science and Technology on Materials Under Shock and Impact, Beijing, 100081, China; Corresponding author. School of Materials Science and Engineering, Beijing Institute of Technology, Beijing, 100081, China.Most recently, high-entropy alloys (HEAs) with 5 or more elements open a new area for materials exploration with substantial mechanical properties. The large composition space and numerous structures of HEAs bring significant difficulties for phase design and determination of mechanical property. Machine learning, one of most rapidly growing scientific and technical field, meets at the intersection of computer science and materials science, and at the center of artificial intelligence. Machine learning provides the opportunity to build up the relationship between multiple physical properties and mechanical properties. The fast changes of this field call for significant practice for materials community to utilize it as a more efficient, accurate and interpretable tool. In this review, we summarize the most promising machine learning models, combined with high-throughput simulation and experimental screening, to predict and fabricate HEAs with desired superb mechanical properties.http://www.sciencedirect.com/science/article/pii/S2238785424020192 |
| spellingShingle | Jianye He Zezhou Li Pingluo Zhao Hongmei Zhang Fan Zhang Lin Wang Xingwang Cheng Machine learning-assisted design of high-entropy alloys with superior mechanical properties Journal of Materials Research and Technology |
| title | Machine learning-assisted design of high-entropy alloys with superior mechanical properties |
| title_full | Machine learning-assisted design of high-entropy alloys with superior mechanical properties |
| title_fullStr | Machine learning-assisted design of high-entropy alloys with superior mechanical properties |
| title_full_unstemmed | Machine learning-assisted design of high-entropy alloys with superior mechanical properties |
| title_short | Machine learning-assisted design of high-entropy alloys with superior mechanical properties |
| title_sort | machine learning assisted design of high entropy alloys with superior mechanical properties |
| url | http://www.sciencedirect.com/science/article/pii/S2238785424020192 |
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