|About this Abstract
||3rd World Congress on High Entropy Alloys (HEA 2023)
||Automated Characterization and Bayesian Prediction of High Strength Multi-principal Element Alloys
||Eddie Gienger, Maitreyee Sharma Priyadarshini, Denise Yin, Lisa Pogue, Justin Rokisky, Paulette Clancy, Christopher Stiles
|On-Site Speaker (Planned)
Multi-principal element alloys (MPEAs) are an active research area for their desirable corrosion resistance, strength-to-weight ratios and high temperature survivability. However, design of new MPEAs with tailored performance is complex, and traditional Edisonian discovery methods are slow. AI-guided discovery is promising for these materials, but there is limited training data. Here, we demonstrate a high-throughput characterization pipeline enabling registration of mechanical properties from nano-indentation with material microstructure. These results, combined with literature data, are used to inform a novel physics-based material discovery framework, PAL 2.0. This new method leverages the advantages of Deep Learning with Bayesian Optimization, making it accessible to the data-scarce field of MPEAs. MPEA recommendations from PAL2.0 are synthesized and characterized to determine yield strength and hardness. This “in-the-loop" computational-experimental approach is one of the first of its kind and has a disruptively attractive potential in the field of materials discovery.
||Planned: Metallurgical and Materials Transactions