{"title":"复杂浓缩合金低模量和高屈服强度的多目标协同设计","authors":"Qingfeng Yin, Yuan Wu, Honghui Wu, Xiaobin Zhang, Suihe Jiang, Hui Wang, Xiongjun Liu, Zhaoping Lu","doi":"10.1038/s41524-025-01713-3","DOIUrl":null,"url":null,"abstract":"<p>Low Young’s modulus and high yield strength are concurrently needed to meet the performance requirements of metallic implant materials. The single-objective performance-oriented alloy design strategies face challenges in effectively addressing the inherent conflict between Young’s modulus and yield strength. In this study, we developed a machine learning model for multi-objective synergistic optimization of modulus and yield strength, successfully enabling simultaneous prediction of Young’s modulus and yield strength in the Ti-Zr-Hf-Nb-Ta-Mo-Sn alloy system. The critical features influencing the modulus and strength of the alloys were systematically analyzed and identified. Moreover, a series of complex concentrated alloy (CCAs) with low Young’s modulus and high yield strength were successfully prepared based on this model. The newly developed alloys exhibited a stable single-phase BCC (body-centered-cubic) structure with Young’s modulus in the range of 40–50 GPa, yield strength of 600–915 MPa, and elastic admissible strain of approximately 1.5%. The multi-objective machine learning model developed in this study can synergistically optimize low Young’s modulus and high yield strength in complex alloys, providing a novel approach for the design of advanced biomedical alloys.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"4 1","pages":""},"PeriodicalIF":11.9000,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A multi-objective synergistic design for low modulus and high yield strength in complex concentrated alloys\",\"authors\":\"Qingfeng Yin, Yuan Wu, Honghui Wu, Xiaobin Zhang, Suihe Jiang, Hui Wang, Xiongjun Liu, Zhaoping Lu\",\"doi\":\"10.1038/s41524-025-01713-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Low Young’s modulus and high yield strength are concurrently needed to meet the performance requirements of metallic implant materials. The single-objective performance-oriented alloy design strategies face challenges in effectively addressing the inherent conflict between Young’s modulus and yield strength. In this study, we developed a machine learning model for multi-objective synergistic optimization of modulus and yield strength, successfully enabling simultaneous prediction of Young’s modulus and yield strength in the Ti-Zr-Hf-Nb-Ta-Mo-Sn alloy system. The critical features influencing the modulus and strength of the alloys were systematically analyzed and identified. Moreover, a series of complex concentrated alloy (CCAs) with low Young’s modulus and high yield strength were successfully prepared based on this model. The newly developed alloys exhibited a stable single-phase BCC (body-centered-cubic) structure with Young’s modulus in the range of 40–50 GPa, yield strength of 600–915 MPa, and elastic admissible strain of approximately 1.5%. The multi-objective machine learning model developed in this study can synergistically optimize low Young’s modulus and high yield strength in complex alloys, providing a novel approach for the design of advanced biomedical alloys.</p>\",\"PeriodicalId\":19342,\"journal\":{\"name\":\"npj Computational Materials\",\"volume\":\"4 1\",\"pages\":\"\"},\"PeriodicalIF\":11.9000,\"publicationDate\":\"2025-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"npj Computational Materials\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1038/s41524-025-01713-3\",\"RegionNum\":1,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Computational Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1038/s41524-025-01713-3","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
A multi-objective synergistic design for low modulus and high yield strength in complex concentrated alloys
Low Young’s modulus and high yield strength are concurrently needed to meet the performance requirements of metallic implant materials. The single-objective performance-oriented alloy design strategies face challenges in effectively addressing the inherent conflict between Young’s modulus and yield strength. In this study, we developed a machine learning model for multi-objective synergistic optimization of modulus and yield strength, successfully enabling simultaneous prediction of Young’s modulus and yield strength in the Ti-Zr-Hf-Nb-Ta-Mo-Sn alloy system. The critical features influencing the modulus and strength of the alloys were systematically analyzed and identified. Moreover, a series of complex concentrated alloy (CCAs) with low Young’s modulus and high yield strength were successfully prepared based on this model. The newly developed alloys exhibited a stable single-phase BCC (body-centered-cubic) structure with Young’s modulus in the range of 40–50 GPa, yield strength of 600–915 MPa, and elastic admissible strain of approximately 1.5%. The multi-objective machine learning model developed in this study can synergistically optimize low Young’s modulus and high yield strength in complex alloys, providing a novel approach for the design of advanced biomedical alloys.
期刊介绍:
npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings.
Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.