Machine learning guided BCC or FCC phase prediction in high entropy alloys
High entropy alloys （HEAs） have excellent properties because they can form simple solid solution (SS) phases, including body-centered cubic (BCC) phase, face-centered cubic (FCC) phase, or FCC + BCC phase, so phase prediction is the first step in alloy design. In current research, machine learning (...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Journal of Materials Research and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785424002588 |
| _version_ | 1797247866027114496 |
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| author | Zhongping He Huan Zhang Hong Cheng Meiling Ge Tianyu Si Lun Che Kaiyuan Zheng Lingrong Zeng Qingyuan Wang |
| author_facet | Zhongping He Huan Zhang Hong Cheng Meiling Ge Tianyu Si Lun Che Kaiyuan Zheng Lingrong Zeng Qingyuan Wang |
| author_sort | Zhongping He |
| collection | DOAJ |
| description | High entropy alloys （HEAs） have excellent properties because they can form simple solid solution (SS) phases, including body-centered cubic (BCC) phase, face-centered cubic (FCC) phase, or FCC + BCC phase, so phase prediction is the first step in alloy design. In current research, machine learning (ML) approach had been widely used to guide the discovery and design of materials. The prediction of HEAs phase structure based on machine learning (ML) is a hot topic. In this work, five ML algorithms were utilized to predict HEAs for SS and amorphous (AM) phases based on 399 collected data sets, including 120 BCC alloys, 87 FCC alloys, 82 BCC + FCC alloys and 110 a.m. alloys. To enhance the model's accuracy, grid search and K-fold cross validation were used to optimize performance. Valence electron concentration (VEC) and ΔHmix exhibit high importance in prediction in compared to other parameters. The results show that the random forest can effectively distinguish BCC phase, FCC phase, mixed solid solution phase (FCC + BCC) and AM, with an accuracy is 0.87. After that, the CoCrFeNiAlx (x = 0, 0.5, 1) system alloys were characterized by XRD and SEM-EDS. The experimental results validated that the phase structure of CoCrFeNiAlx alloys changed from FCC to BCC + FCC and BCC with the increase of Al content, which is consistent with the ML prediction. |
| first_indexed | 2024-03-08T00:00:38Z |
| format | Article |
| id | doaj.art-447822d926214e749f87245d1248e37d |
| institution | Directory Open Access Journal |
| issn | 2238-7854 |
| language | English |
| last_indexed | 2024-04-24T20:05:30Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj.art-447822d926214e749f87245d1248e37d2024-03-24T06:57:48ZengElsevierJournal of Materials Research and Technology2238-78542024-03-012934773486Machine learning guided BCC or FCC phase prediction in high entropy alloysZhongping He0Huan Zhang1Hong Cheng2Meiling Ge3Tianyu Si4Lun Che5Kaiyuan Zheng6Lingrong Zeng7Qingyuan Wang8School of Mechanical Engineering, Chengdu University, Chengdu, 610106, China; Corresponding author.School of Mechanical Engineering, Chengdu University, Chengdu, 610106, ChinaSchool of Mechanical Engineering, Chengdu University, Chengdu, 610106, ChinaSchool of Mechanical Engineering, Chengdu University, Chengdu, 610106, ChinaSchool of Mechanical Engineering, Chengdu University, Chengdu, 610106, ChinaSchool of Mechanical Engineering, Chengdu University, Chengdu, 610106, ChinaSchool of Mechanical Engineering, Chengdu University, Chengdu, 610106, ChinaSchool of Mechanical Engineering, Chengdu University, Chengdu, 610106, ChinaSchool of Mechanical Engineering, Chengdu University, Chengdu, 610106, China; Institute for Advanced Study, Chengdu University, Chengdu, 610106, China; Corresponding author. School of Mechanical Engineering, Chengdu University, Chengdu, 610106, ChinaHigh entropy alloys （HEAs） have excellent properties because they can form simple solid solution (SS) phases, including body-centered cubic (BCC) phase, face-centered cubic (FCC) phase, or FCC + BCC phase, so phase prediction is the first step in alloy design. In current research, machine learning (ML) approach had been widely used to guide the discovery and design of materials. The prediction of HEAs phase structure based on machine learning (ML) is a hot topic. In this work, five ML algorithms were utilized to predict HEAs for SS and amorphous (AM) phases based on 399 collected data sets, including 120 BCC alloys, 87 FCC alloys, 82 BCC + FCC alloys and 110 a.m. alloys. To enhance the model's accuracy, grid search and K-fold cross validation were used to optimize performance. Valence electron concentration (VEC) and ΔHmix exhibit high importance in prediction in compared to other parameters. The results show that the random forest can effectively distinguish BCC phase, FCC phase, mixed solid solution phase (FCC + BCC) and AM, with an accuracy is 0.87. After that, the CoCrFeNiAlx (x = 0, 0.5, 1) system alloys were characterized by XRD and SEM-EDS. The experimental results validated that the phase structure of CoCrFeNiAlx alloys changed from FCC to BCC + FCC and BCC with the increase of Al content, which is consistent with the ML prediction.http://www.sciencedirect.com/science/article/pii/S2238785424002588Machine learningHigh entropy alloysPhase structurePrediction |
| spellingShingle | Zhongping He Huan Zhang Hong Cheng Meiling Ge Tianyu Si Lun Che Kaiyuan Zheng Lingrong Zeng Qingyuan Wang Machine learning guided BCC or FCC phase prediction in high entropy alloys Journal of Materials Research and Technology Machine learning High entropy alloys Phase structure Prediction |
| title | Machine learning guided BCC or FCC phase prediction in high entropy alloys |
| title_full | Machine learning guided BCC or FCC phase prediction in high entropy alloys |
| title_fullStr | Machine learning guided BCC or FCC phase prediction in high entropy alloys |
| title_full_unstemmed | Machine learning guided BCC or FCC phase prediction in high entropy alloys |
| title_short | Machine learning guided BCC or FCC phase prediction in high entropy alloys |
| title_sort | machine learning guided bcc or fcc phase prediction in high entropy alloys |
| topic | Machine learning High entropy alloys Phase structure Prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2238785424002588 |
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