Using Materials Informatics to Quantify Complex Correlations Linking Structure, Properties and Processing in Materials
I will present several examples in which materials informatics can be used to elucidate and quantify complex correlations linking structure, properties and processing of materials. In the first example, I consider the case of high-entropy (HE) (or multi-principal element) alloys, typically comprising five or more elements. The study of these alloys is a relatively new area of materials research that has 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. 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. In the second example, I consider the ubiquitous phenomenon of grain abnormality in a microstructure that involves the unusually rapid growth of a minority of constituent grains, with the resulting bimodal structure often having a deleterious impact on the thermomechanical properties of a system. In this context, I will describe the use of CCA in conjunction with the formalism of extreme-value statistics to correlate abnormal grain growth with powder processing and chemistry for specialty ceramics. Finally, I will outline the use of detrended correlation analyses to interpret time series data associated with ceramic powder processing.