Combining Machine Learning and Molecular Dynamics to Predict Mechanical Properties and Microstructural Evolution of FeNiCrCoCu High-Entropy Alloys
Compared with traditional alloys, high-entropy alloys have better mechanical properties and corrosion resistance. However, their mechanical properties and microstructural evolution behavior are unclear due to their complex composition. Machine learning has powerful data processing and analysis capab...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Nanomaterials |
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| Online Access: | https://www.mdpi.com/2079-4991/13/6/968 |
| _version_ | 1797609752553848832 |
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| author | Jingui Yu Faping Yu Qiang Fu Gang Zhao Caiyun Gong Mingchao Wang Qiaoxin Zhang |
| author_facet | Jingui Yu Faping Yu Qiang Fu Gang Zhao Caiyun Gong Mingchao Wang Qiaoxin Zhang |
| author_sort | Jingui Yu |
| collection | DOAJ |
| description | Compared with traditional alloys, high-entropy alloys have better mechanical properties and corrosion resistance. However, their mechanical properties and microstructural evolution behavior are unclear due to their complex composition. Machine learning has powerful data processing and analysis capabilities, that provides technical advantages for in-depth study of the mechanical properties of high-entropy alloys. Thus, we combined machine learning and molecular dynamics to predict the mechanical properties of FeNiCrCoCu high-entropy alloys. The optimal multiple linear regression machine learning algorithm predicts that the optimal composition is Fe<sub>33</sub>Ni<sub>32</sub>Cr<sub>11</sub>Co<sub>11</sub>Cu<sub>13</sub> high-entropy alloy, with a tensile strength of 28.25 GPa. Furthermore, molecular dynamics is used to verify the predicted mechanical properties of high-entropy alloys, and it is found that the error between the tensile strength predicted by machine learning and the tensile strength obtained by molecular dynamics simulation is within 0.5%. Moreover, the tensile-compression asymmetry of Fe<sub>33</sub>Ni<sub>32</sub>Cr<sub>11</sub>Co<sub>11</sub>Cu<sub>13</sub> high-entropy alloy increased with the increase of temperature and Cu content and the decrease of Fe content. This is due to the increase in stress caused by twinning during compression and the decrease in stress due to dislocation slip during stretching. Interestingly, high-entropy alloy coatings reduce the tensile-compression asymmetry of nickel; this is attributed to the reduced influence of dislocations and twinning at the interface between the high-entropy alloy and the nickel matrix. |
| first_indexed | 2024-03-11T06:05:46Z |
| format | Article |
| id | doaj.art-0471fba95f5d43209c7844da2ba4d4d6 |
| institution | Directory Open Access Journal |
| issn | 2079-4991 |
| language | English |
| last_indexed | 2024-03-11T06:05:46Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nanomaterials |
| spelling | doaj.art-0471fba95f5d43209c7844da2ba4d4d62023-11-17T12:59:39ZengMDPI AGNanomaterials2079-49912023-03-0113696810.3390/nano13060968Combining Machine Learning and Molecular Dynamics to Predict Mechanical Properties and Microstructural Evolution of FeNiCrCoCu High-Entropy AlloysJingui Yu0Faping Yu1Qiang Fu2Gang Zhao3Caiyun Gong4Mingchao Wang5Qiaoxin Zhang6School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, ChinaWuhan Institute of Marine Electric Propulsion, Wuhan 430064, ChinaHubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaHubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaCentre for Theoretical and Computational Molecular Science, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, QLD 4072, AustraliaSchool of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, ChinaCompared with traditional alloys, high-entropy alloys have better mechanical properties and corrosion resistance. However, their mechanical properties and microstructural evolution behavior are unclear due to their complex composition. Machine learning has powerful data processing and analysis capabilities, that provides technical advantages for in-depth study of the mechanical properties of high-entropy alloys. Thus, we combined machine learning and molecular dynamics to predict the mechanical properties of FeNiCrCoCu high-entropy alloys. The optimal multiple linear regression machine learning algorithm predicts that the optimal composition is Fe<sub>33</sub>Ni<sub>32</sub>Cr<sub>11</sub>Co<sub>11</sub>Cu<sub>13</sub> high-entropy alloy, with a tensile strength of 28.25 GPa. Furthermore, molecular dynamics is used to verify the predicted mechanical properties of high-entropy alloys, and it is found that the error between the tensile strength predicted by machine learning and the tensile strength obtained by molecular dynamics simulation is within 0.5%. Moreover, the tensile-compression asymmetry of Fe<sub>33</sub>Ni<sub>32</sub>Cr<sub>11</sub>Co<sub>11</sub>Cu<sub>13</sub> high-entropy alloy increased with the increase of temperature and Cu content and the decrease of Fe content. This is due to the increase in stress caused by twinning during compression and the decrease in stress due to dislocation slip during stretching. Interestingly, high-entropy alloy coatings reduce the tensile-compression asymmetry of nickel; this is attributed to the reduced influence of dislocations and twinning at the interface between the high-entropy alloy and the nickel matrix.https://www.mdpi.com/2079-4991/13/6/968high entropy alloymachine learningmolecular dynamicsmechanical properties |
| spellingShingle | Jingui Yu Faping Yu Qiang Fu Gang Zhao Caiyun Gong Mingchao Wang Qiaoxin Zhang Combining Machine Learning and Molecular Dynamics to Predict Mechanical Properties and Microstructural Evolution of FeNiCrCoCu High-Entropy Alloys Nanomaterials high entropy alloy machine learning molecular dynamics mechanical properties |
| title | Combining Machine Learning and Molecular Dynamics to Predict Mechanical Properties and Microstructural Evolution of FeNiCrCoCu High-Entropy Alloys |
| title_full | Combining Machine Learning and Molecular Dynamics to Predict Mechanical Properties and Microstructural Evolution of FeNiCrCoCu High-Entropy Alloys |
| title_fullStr | Combining Machine Learning and Molecular Dynamics to Predict Mechanical Properties and Microstructural Evolution of FeNiCrCoCu High-Entropy Alloys |
| title_full_unstemmed | Combining Machine Learning and Molecular Dynamics to Predict Mechanical Properties and Microstructural Evolution of FeNiCrCoCu High-Entropy Alloys |
| title_short | Combining Machine Learning and Molecular Dynamics to Predict Mechanical Properties and Microstructural Evolution of FeNiCrCoCu High-Entropy Alloys |
| title_sort | combining machine learning and molecular dynamics to predict mechanical properties and microstructural evolution of fenicrcocu high entropy alloys |
| topic | high entropy alloy machine learning molecular dynamics mechanical properties |
| url | https://www.mdpi.com/2079-4991/13/6/968 |
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