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Machine Learning for Predicting the Critical Yield Stress of High Entropy Alloys by Pau Cutrina Vilalta; Somayyeh Sheikholeslami; Katerine Saleme Ruiz; Xin C. Yee; Marisol Koslowski is a Engineering article available to read on EtoBox.

What is Machine Learning for Predicting the Critical Yield Stress of High Entropy Alloys about?

## 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. 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.

Who reads Machine Learning for Predicting the Critical Yield Stress of High Entropy Alloys?

Author

Pau Cutrina Vilalta; Somayyeh Sheikholeslami; Katerine Saleme Ruiz; Xin C. Yee; Marisol Koslowski

Publisher

ASME International

Published

2020

Language

EN

Field

Engineering (Physical Sciences)