Theory-guided design of high-strength, ductile multi-principal-element alloys within physics-based metrics for machine-learning

For accelerated design of multiple-principal-elements alloys (MPEAs) as promising materials for next-generation energy technologies, we present a rapid theory-guided down-selection for combinatorial synthesis of high-temperature MPEAs having high-strength and ductility. We showcase simple physics-based metrics to predict and to assess rapidly properties for arbitrary metals and solid-solution alloys, in particular strength and ductility. For example, the intrinsic strength of any solid-phase metal (single- and poly-crystal and amorphous) is obtained directly from an electronic metric available from any density-functional theory (DFT) code. For design, we showcase these predictions to inform bulk combinatorial synthesis and characterization to verify down-selection of superior mechanical properties, or other properties including catalysis. Examples for numerous systems will be presented.