{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T05:37:09Z","timestamp":1775367429723,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T00:00:00Z","timestamp":1699401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["U2267252"],"award-info":[{"award-number":["U2267252"]}]},{"name":"National Natural Science Foundation of China","award":["12372069"],"award-info":[{"award-number":["12372069"]}]},{"name":"National Natural Science Foundation of China","award":["12172123"],"award-info":[{"award-number":["12172123"]}]},{"name":"National Natural Science Foundation of China","award":["2022JJ20001"],"award-info":[{"award-number":["2022JJ20001"]}]},{"name":"National Natural Science Foundation of China","award":["CX20220378"],"award-info":[{"award-number":["CX20220378"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["U2267252"],"award-info":[{"award-number":["U2267252"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["12372069"],"award-info":[{"award-number":["12372069"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["12172123"],"award-info":[{"award-number":["12172123"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["2022JJ20001"],"award-info":[{"award-number":["2022JJ20001"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["CX20220378"],"award-info":[{"award-number":["CX20220378"]}]},{"name":"Hunan Provincial Innovation Foundation for Postgraduate","award":["U2267252"],"award-info":[{"award-number":["U2267252"]}]},{"name":"Hunan Provincial Innovation Foundation for Postgraduate","award":["12372069"],"award-info":[{"award-number":["12372069"]}]},{"name":"Hunan Provincial Innovation Foundation for Postgraduate","award":["12172123"],"award-info":[{"award-number":["12172123"]}]},{"name":"Hunan Provincial Innovation Foundation for Postgraduate","award":["2022JJ20001"],"award-info":[{"award-number":["2022JJ20001"]}]},{"name":"Hunan Provincial Innovation Foundation for Postgraduate","award":["CX20220378"],"award-info":[{"award-number":["CX20220378"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Obtaining a suitable chemical composition for high-entropy alloys (HEAs) with superior mechanical properties and good biocompatibility is still a formidable challenge through conventional trial-and-error methods. Here, based on a large amount of experimental data, a machine learning technique may be used to establish the relationship between the composition and the mechanical properties of the biocompatible HEAs. Subsequently, first-principles calculations are performed to verify the accuracy of the prediction results from the machine learning model. The predicted Young\u2019s modulus and yield strength of HEAs performed very well in the previous experiments. In addition, the effect on the mechanical properties of alloying an element is investigated in the selected Ti-Zr-Hf-Nb-Ta HEA with the high crystal symmetry. Finally, the Ti8-Zr20-Hf16-Nb35-Ta21 HEA predicted by the machine learning model exhibits a good combination of biocompatibility and mechanical performance, attributed to a significant electron flow and charge recombination. This work reveals the importance of these strategies, combined with machine learning and first-principles calculations, on the development of advanced biocompatible HEAs.<\/jats:p>","DOI":"10.3390\/sym15112029","type":"journal-article","created":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T07:02:05Z","timestamp":1699426925000},"page":"2029","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Chemical Composition Optimization of Biocompatible Non-Equiatomic High-Entropy Alloys Using Machine Learning and First-Principles Calculations"],"prefix":"10.3390","volume":"15","author":[{"given":"Gengzhu","family":"Zhou","sequence":"first","affiliation":[{"name":"State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zili","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8553-2024","authenticated-orcid":false,"given":"Renyao","family":"Feng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjie","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenyou","family":"Peng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feifei","family":"Fan","sequence":"additional","affiliation":[{"name":"Mechanical Engineering Department, University of Nevada, 1664 N Virginia St., Reno, NV 89551, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qihong","family":"Fang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1557\/s43579-021-00045-9","article-title":"Coupling high-throughput experiment and machine learning to optimize elemental composition in nickel-based superalloys","volume":"11","author":"Wang","year":"2021","journal-title":"MRS Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"413078","DOI":"10.1016\/j.physb.2021.413078","article-title":"Effect of interstitial N atom on physical and mechanical properties of FeCoCrNiMn high-entropy alloys: A first-principles study","volume":"615","author":"Chen","year":"2021","journal-title":"Phys. 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