Automated Database Generation of Multi-Principal Element Alloy Phase-Specific Mechanical Properties Measured with Nano-Indentation

Multi-principal metal alloys (MPEAs) are an active research area for their desirable mechanical performance and electrochemical resistance. However, design of new MPEAs is complex. The potential composition space for MPEAs is massive and Edisonian approaches are slow. Artificial intelligence (AI) guided discovery is promising for these materials, but there is limited training data. Here, we demonstrate a high-throughput characterization pipeline capable of registering mechanical properties from nano-indentation with material microstructure. Fifty MPEAs with refractory and high temperature shape memory behavior were arc melted for rapid evaluation. Compositional maps of microstructures were determined with microscopy and energy dispersive X-ray spectroscopy. These maps were segmented and automatically registered with indentation locations. Information relevant to mechanical properties such as phase composition and phase boundaries were extracted and added to a running database for future forward property predictions. The application of this technique to properties like high temperature shape memory performance and corrosion resistance is explored.