| Abstract Scope |
I will describe the use of materials informatics to elucidate and quantify complex correlations linking the structure, properties and processing of high-entropy (HE) (or multi-principal element) alloys. Such alloys, typically comprising five or more elements, have attracted intense interest in recent years as, in many cases, these systems possess unexpected and superior mechanical (and other) properties relative to those of conventional alloys. However, the identification of promising HE alloys presents a daunting challenge given the associated vastness of the chemistry/composition space. In particular, I will describe a supervised learning strategy for the efficient screening of HE alloys that combines two complementary tools, namely: (1) a canonical-correlation analysis (CCA) and (2) a genetic algorithm (GA) with a CCA-inspired fitness function. The aim is to identify promising new alloys having high hardness values. Mechanical testing and microscopy were used to validate our predictions and to provide insight into associated strengthening mechanisms. |