Chemical Composition Optimization of Biocompatible Non-Equiatomic High-Entropy Alloys Using Machine Learning and First-Principles Calculations
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...
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| Format: | Article |
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MDPI AG
2023-11-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/15/11/2029 |
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| author | Gengzhu Zhou Zili Zhang Renyao Feng Wenjie Zhao Shenyou Peng Jia Li Feifei Fan Qihong Fang |
| author_facet | Gengzhu Zhou Zili Zhang Renyao Feng Wenjie Zhao Shenyou Peng Jia Li Feifei Fan Qihong Fang |
| author_sort | Gengzhu Zhou |
| collection | DOAJ |
| description | 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’s 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 Ti<sub>8</sub>-Zr<sub>20</sub>-Hf<sub>16</sub>-Nb<sub>35</sub>-Ta<sub>21</sub> 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. |
| first_indexed | 2024-03-09T16:24:40Z |
| format | Article |
| id | doaj.art-7e46cab14f6f4c5a9d136defd2378620 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T16:24:40Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-7e46cab14f6f4c5a9d136defd23786202023-11-24T15:08:49ZengMDPI AGSymmetry2073-89942023-11-011511202910.3390/sym15112029Chemical Composition Optimization of Biocompatible Non-Equiatomic High-Entropy Alloys Using Machine Learning and First-Principles CalculationsGengzhu Zhou0Zili Zhang1Renyao Feng2Wenjie Zhao3Shenyou Peng4Jia Li5Feifei Fan6Qihong Fang7State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, ChinaState Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, ChinaState Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, ChinaState Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, ChinaState Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, ChinaState Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, ChinaMechanical Engineering Department, University of Nevada, 1664 N Virginia St., Reno, NV 89551, USAState Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, ChinaObtaining 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’s 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 Ti<sub>8</sub>-Zr<sub>20</sub>-Hf<sub>16</sub>-Nb<sub>35</sub>-Ta<sub>21</sub> 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.https://www.mdpi.com/2073-8994/15/11/2029biocompatibilityfirst-principles calculationhigh-entropy alloymachine learningmechanical properties |
| spellingShingle | Gengzhu Zhou Zili Zhang Renyao Feng Wenjie Zhao Shenyou Peng Jia Li Feifei Fan Qihong Fang Chemical Composition Optimization of Biocompatible Non-Equiatomic High-Entropy Alloys Using Machine Learning and First-Principles Calculations Symmetry biocompatibility first-principles calculation high-entropy alloy machine learning mechanical properties |
| title | Chemical Composition Optimization of Biocompatible Non-Equiatomic High-Entropy Alloys Using Machine Learning and First-Principles Calculations |
| title_full | Chemical Composition Optimization of Biocompatible Non-Equiatomic High-Entropy Alloys Using Machine Learning and First-Principles Calculations |
| title_fullStr | Chemical Composition Optimization of Biocompatible Non-Equiatomic High-Entropy Alloys Using Machine Learning and First-Principles Calculations |
| title_full_unstemmed | Chemical Composition Optimization of Biocompatible Non-Equiatomic High-Entropy Alloys Using Machine Learning and First-Principles Calculations |
| title_short | Chemical Composition Optimization of Biocompatible Non-Equiatomic High-Entropy Alloys Using Machine Learning and First-Principles Calculations |
| title_sort | chemical composition optimization of biocompatible non equiatomic high entropy alloys using machine learning and first principles calculations |
| topic | biocompatibility first-principles calculation high-entropy alloy machine learning mechanical properties |
| url | https://www.mdpi.com/2073-8994/15/11/2029 |
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