Phase Prediction and Visualized Design Process of High Entropy Alloys via Machine Learned Methodology
High entropy alloys, which contain five or more elements in equal atomic concentrations, tend to exhibit remarkable mechanical and physical properties that are typically dependent on their phase constitution. In this work, a based leaner and four ensemble machine learning models are carried out to p...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/13/2/283 |
| _version_ | 1797619332116643840 |
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| author | Jin Gao Yifan Wang Jianxin Hou Junhua You Keqiang Qiu Suode Zhang Jianqiang Wang |
| author_facet | Jin Gao Yifan Wang Jianxin Hou Junhua You Keqiang Qiu Suode Zhang Jianqiang Wang |
| author_sort | Jin Gao |
| collection | DOAJ |
| description | High entropy alloys, which contain five or more elements in equal atomic concentrations, tend to exhibit remarkable mechanical and physical properties that are typically dependent on their phase constitution. In this work, a based leaner and four ensemble machine learning models are carried out to predict the phase of high entropy alloys in a database consisting of 511 labeled data. Before the models are trained, features based on the empirical design principles are selected through XGBoost, taking into account the relative importance of each feature. The ensemble learning methods of Voting and Stacking stand out among these algorithms, with a predictive accuracy of over 92%. In addition, the alloy designing process is visualized by a decision tree, introducing a new criterion for identifying phases of FCC, BCC, and FCC + BCC in high entropy alloys. These findings provide valuable information for selecting important features and suitable machine learning models in the design of high entropy alloys. |
| first_indexed | 2024-03-11T08:25:32Z |
| format | Article |
| id | doaj.art-c3637ac16abc42efb5ce554c327ad4ec |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-11T08:25:32Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-c3637ac16abc42efb5ce554c327ad4ec2023-11-16T22:07:12ZengMDPI AGMetals2075-47012023-01-0113228310.3390/met13020283Phase Prediction and Visualized Design Process of High Entropy Alloys via Machine Learned MethodologyJin Gao0Yifan Wang1Jianxin Hou2Junhua You3Keqiang Qiu4Suode Zhang5Jianqiang Wang6School of Materials Science and Engineering, Shenyang University of Technology, Shenyang 110870, ChinaShenyang National Laboratory for Materials Science, Institute of Metal Research, CAS, Shenyang 110016, ChinaNational Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang 110819, ChinaSchool of Materials Science and Engineering, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Materials Science and Engineering, Shenyang University of Technology, Shenyang 110870, ChinaShenyang National Laboratory for Materials Science, Institute of Metal Research, CAS, Shenyang 110016, ChinaShenyang National Laboratory for Materials Science, Institute of Metal Research, CAS, Shenyang 110016, ChinaHigh entropy alloys, which contain five or more elements in equal atomic concentrations, tend to exhibit remarkable mechanical and physical properties that are typically dependent on their phase constitution. In this work, a based leaner and four ensemble machine learning models are carried out to predict the phase of high entropy alloys in a database consisting of 511 labeled data. Before the models are trained, features based on the empirical design principles are selected through XGBoost, taking into account the relative importance of each feature. The ensemble learning methods of Voting and Stacking stand out among these algorithms, with a predictive accuracy of over 92%. In addition, the alloy designing process is visualized by a decision tree, introducing a new criterion for identifying phases of FCC, BCC, and FCC + BCC in high entropy alloys. These findings provide valuable information for selecting important features and suitable machine learning models in the design of high entropy alloys.https://www.mdpi.com/2075-4701/13/2/283machine learninghigh entropy alloysensemble methodsphase predictionvisualized process |
| spellingShingle | Jin Gao Yifan Wang Jianxin Hou Junhua You Keqiang Qiu Suode Zhang Jianqiang Wang Phase Prediction and Visualized Design Process of High Entropy Alloys via Machine Learned Methodology Metals machine learning high entropy alloys ensemble methods phase prediction visualized process |
| title | Phase Prediction and Visualized Design Process of High Entropy Alloys via Machine Learned Methodology |
| title_full | Phase Prediction and Visualized Design Process of High Entropy Alloys via Machine Learned Methodology |
| title_fullStr | Phase Prediction and Visualized Design Process of High Entropy Alloys via Machine Learned Methodology |
| title_full_unstemmed | Phase Prediction and Visualized Design Process of High Entropy Alloys via Machine Learned Methodology |
| title_short | Phase Prediction and Visualized Design Process of High Entropy Alloys via Machine Learned Methodology |
| title_sort | phase prediction and visualized design process of high entropy alloys via machine learned methodology |
| topic | machine learning high entropy alloys ensemble methods phase prediction visualized process |
| url | https://www.mdpi.com/2075-4701/13/2/283 |
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