|About this Abstract
||3rd World Congress on High Entropy Alloys (HEA 2023)
||ML-based High-throughput Search to Identify Refractory High Entropy Alloy with Trade-off Mechanical Properties
||Debasis Sengupta, Stephen Giles, Hugh Shortt, Peter Liaw
|On-Site Speaker (Planned)
Identifying Refractory High Entropy Alloy (RHEA) compositions with desired high temperature strength and room temperature ductility/plasticity from the vast composition space is a challenging task. Generally, compositions with higher yield strength tend to have lower room temperature ductility or sometime show brittle behavior. In this work, we first present machine learning (ML) based models for compressive yield strength and room temperature plasticity. These models were extensively validated against experiments. The two completely independent models were able to reproduce the well-established fact that an increase in plasticity comes at a cost of reduction in strength. We then used the two models and applied the state-of-the-art sampling method to generated approximately 100,000 RHEA compositions, and computed their strengths and plasticities. We then designed a “Figure of Merit” to identify the promising compositions. Some selected compositions were synthesized and characterized for their mechanical properties.
||Planned: Metallurgical and Materials Transactions