Machine Learning for Predicting the Critical Yield Stress of High Entropy Alloys
We applied machine learning models to predict the relationship between the yield stress and the stacking fault energies landscape in high entropy alloys. The data for learning in this work were taken from phase-field dislocation dynamics simulations of partial dislocations in face-centered-cubic met...
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| Veröffentlicht in: | Journal of engineering materials and technology 2021-04, Vol.143 (2), Article 021005 |
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| container_title | Journal of engineering materials and technology |
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| creator | Vilalta, Pau Cutrina Sheikholeslami, Somayyeh Saleme Ruiz, Katerine C. Yee, Xin Koslowski, Marisol |
| description | We applied machine learning models to predict the relationship between the yield stress and the stacking fault energies landscape in high entropy alloys. The data for learning in this work were taken from phase-field dislocation dynamics simulations of partial dislocations in face-centered-cubic metals. This study was motivated by the intensive computation required for phase-field simulations. We adopted three different ways to describe the variations of the stacking fault energy (SFE) landscape as inputs to the machine learning models. Our study showed that the best machine learning model was able to predict the yield stress to approximately 2% error. In addition, our unsupervised learning study produced a principal component that showed the same trend as a physically meaningful quantity with respect to the critical yield stress. |
| doi_str_mv | 10.1115/1.4048873 |
| format | Article |
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Technol</stitle><stitle>J ENG MATER-T ASME</stitle><date>2021-04-01</date><risdate>2021</risdate><volume>143</volume><issue>2</issue><artnum>021005</artnum><issn>0094-4289</issn><eissn>1528-8889</eissn><abstract>We applied machine learning models to predict the relationship between the yield stress and the stacking fault energies landscape in high entropy alloys. The data for learning in this work were taken from phase-field dislocation dynamics simulations of partial dislocations in face-centered-cubic metals. This study was motivated by the intensive computation required for phase-field simulations. We adopted three different ways to describe the variations of the stacking fault energy (SFE) landscape as inputs to the machine learning models. Our study showed that the best machine learning model was able to predict the yield stress to approximately 2% error. 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| subjects | Engineering Engineering, Mechanical Materials Science Materials Science, Multidisciplinary Science & Technology Technology |
| title | Machine Learning for Predicting the Critical Yield Stress of High Entropy Alloys |
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