| About this Abstract |
| Meeting |
6th World Congress on Integrated Computational Materials Engineering (ICME 2022)
|
| Symposium
|
6th World Congress on Integrated Computational Materials Engineering (ICME 2022)
|
| Presentation Title |
Machine Learning Assisted Yield Strength and Hardness Prediction of Multi-Principal Element Alloys |
| Author(s) |
Mohammad Fuad Nur Taufique, Ankit Roy, Ganesh Balasubramanian, Gaoyuan Ouyang, Duane D. Johnson, Ram Devanathan |
| On-Site Speaker (Planned) |
Mohammad Fuad Nur Taufique |
| Abstract Scope |
Multi-Principal Element Alloys (MPEAs) have better properties, such as yield strength, hardness, and corrosion resistance compared to conventional alloys. Compositional optimization is a challenging task to obtain desired properties of MPEAs and machine learning is a potential tool to rapidly accelerate the search and design of new materials. We have implemented machine learning tools to predict the yield strength and Vickers hardness of MPEAs at room temperature by employing gradient boost regression (GBR) algorithm. Our results suggest that valence electron concentration (VEC) is the key feature dominating the yield strength and hardness of MPEAs. Our predicted yield strength and hardness values on experimental validation set show <10 % error with respect to the actual values. We believe that our machine learning model will act as a swift tool for screening the half a trillion large search space of MPEAs and down select promising compositions for useful applications. |
| Proceedings Inclusion? |
Definite: Other |