A multi‐objective feature optimization strategy for developing high‐entropy alloys with optimal strength and ductility
Abstract Selecting appropriate material features is essential for effective data‐driven materials design. Here, we propose a multi‐objective feature optimization strategy that identifies feature subsets to improve both prediction accuracy and active learning efficiency for iterative experimentation....
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2025-03-01
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| Series: | Materials Genome Engineering Advances |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/mgea.70000 |
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| _version_ | 1850047084271501312 |
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| author | Yan Zhang Shewei Xin Wei Zhou Xiao Wang Yangyang Xu Yanjing Su |
| author_facet | Yan Zhang Shewei Xin Wei Zhou Xiao Wang Yangyang Xu Yanjing Su |
| author_sort | Yan Zhang |
| collection | DOAJ |
| description | Abstract Selecting appropriate material features is essential for effective data‐driven materials design. Here, we propose a multi‐objective feature optimization strategy that identifies feature subsets to improve both prediction accuracy and active learning efficiency for iterative experimentation. Our approach integrates an evolutionary genetic algorithm to explore an expanded feature space, encompassing both traditional feature pools and a continuous numerical representation of elements rather than relying solely on discrete values. We demonstrate this strategy by identifying high‐entropy alloys (HEAs) with optimal strength and ductility. Results show that the optimized feature subsets reduce prediction errors by 20% for strength and 11% for ductility. Additionally, within fewer than three feedback iterations, HEAs with outstanding combinations of yield strength and ductility are identified, highlighting the high efficiency of this approach. This multi‐objective feature optimization strategy is adaptable to other material systems, offering a pathway to improve machine learning performance and accelerate materials discovery. |
| format | Article |
| id | doaj-art-af929a4dfa944db0ab7dc9f1d0b8c96b |
| institution | DOAJ |
| issn | 2940-9489 2940-9497 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Materials Genome Engineering Advances |
| spelling | doaj-art-af929a4dfa944db0ab7dc9f1d0b8c96b2025-08-20T02:54:18ZengWiley-VCHMaterials Genome Engineering Advances2940-94892940-94972025-03-0131n/an/a10.1002/mgea.70000A multi‐objective feature optimization strategy for developing high‐entropy alloys with optimal strength and ductilityYan Zhang0Shewei Xin1Wei Zhou2Xiao Wang3Yangyang Xu4Yanjing Su5Northwest Institute for Non‐ferrous Metal Research Xi'an ChinaNorthwest Institute for Non‐ferrous Metal Research Xi'an ChinaNorthwest Institute for Non‐ferrous Metal Research Xi'an ChinaNorthwest Institute for Non‐ferrous Metal Research Xi'an ChinaState Key Laboratory for Mechanical Behavior of Materials Xi'an Jiaotong University Xi'an ChinaBeijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing ChinaAbstract Selecting appropriate material features is essential for effective data‐driven materials design. Here, we propose a multi‐objective feature optimization strategy that identifies feature subsets to improve both prediction accuracy and active learning efficiency for iterative experimentation. Our approach integrates an evolutionary genetic algorithm to explore an expanded feature space, encompassing both traditional feature pools and a continuous numerical representation of elements rather than relying solely on discrete values. We demonstrate this strategy by identifying high‐entropy alloys (HEAs) with optimal strength and ductility. Results show that the optimized feature subsets reduce prediction errors by 20% for strength and 11% for ductility. Additionally, within fewer than three feedback iterations, HEAs with outstanding combinations of yield strength and ductility are identified, highlighting the high efficiency of this approach. This multi‐objective feature optimization strategy is adaptable to other material systems, offering a pathway to improve machine learning performance and accelerate materials discovery.https://doi.org/10.1002/mgea.70000feature engineeringhigh entropy alloysmachine learningmaterials featuresmulti‐objective feature optimization |
| spellingShingle | Yan Zhang Shewei Xin Wei Zhou Xiao Wang Yangyang Xu Yanjing Su A multi‐objective feature optimization strategy for developing high‐entropy alloys with optimal strength and ductility Materials Genome Engineering Advances feature engineering high entropy alloys machine learning materials features multi‐objective feature optimization |
| title | A multi‐objective feature optimization strategy for developing high‐entropy alloys with optimal strength and ductility |
| title_full | A multi‐objective feature optimization strategy for developing high‐entropy alloys with optimal strength and ductility |
| title_fullStr | A multi‐objective feature optimization strategy for developing high‐entropy alloys with optimal strength and ductility |
| title_full_unstemmed | A multi‐objective feature optimization strategy for developing high‐entropy alloys with optimal strength and ductility |
| title_short | A multi‐objective feature optimization strategy for developing high‐entropy alloys with optimal strength and ductility |
| title_sort | multi objective feature optimization strategy for developing high entropy alloys with optimal strength and ductility |
| topic | feature engineering high entropy alloys machine learning materials features multi‐objective feature optimization |
| url | https://doi.org/10.1002/mgea.70000 |
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