| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
AI/Data informatics: Design of Structural Materials
|
| Presentation Title |
Machine Learning Assisted Exploration of FeCoCrNi Based Nanocrystal-amorphous Dual-phase Alloys |
| Author(s) |
Yi Yao, Xiaobing Hu, Xiaoxiang Yu, Jiaqi Gong, Feng Yan, Lin Li |
| On-Site Speaker (Planned) |
Yi Yao |
| Abstract Scope |
Alloys with nanocrystal-amorphous dual-phase structure are elaborately designed to promote strength and ductility synergy. The lack of a physical understanding of their formation mechanism, however, makes the alloy design exceedingly laborious. In this study, we utilize machine learning (ML) models with three algorithms (i.e. artificial neural network, logistic regression, and support vector machine) to search multicomponent nanocrystal-amorphous dual-phase alloys. The models are trained on electronic, atomic, thermodynamic features of 5527 alloys. The cross-validation of the three ML algorithms demonstrates the artificial neural network has the highest performance, the area under curve is 0.9889. The model is then used to explore FeCoCrNi based alloy system, and FeCoCrNi-Mo alloys have been predicted to form the dual-phase structure, and then validated experimentally. We further perform a detailed analysis of data features that dominate the dual-phase formation for different synthesis methods, gaining insights into the controlling features that will accelerate the dual-phase alloy discovery. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, High-Entropy Alloys, Thin Films and Interfaces |