Self-supervised optimization of random material microstructures in the small-data regime

While the forward and backward modeling of the process-structure-property chain has received a lot of attention from the materials’ community,fewer efforts have taken into consideration uncertainties.Those arise from a multitude of sources and their quantification and integration in the inversion process are essential in meeting the materials design objectives.The first contribution of this paper is a flexible,fully probabilistic formulation of materials’ optimization problems that accounts for the uncertainty in the process-structure and structure-property linkages and enables the identification of optimal,high-dimensional,process parameters.We employ a probabilistic,data-driven surrogate for the structure-property link which expedites computations and enables handling of non-differential objectives.We couple this with a problem-tailored active learning strategy,i.e.,a self-supervised selection of training data,which significantly improves accuracy while reducing the number of expensive model simulations.We demonstrate its efficacy in optimizing the mechanical and thermal properties of two-phase,random media but envision that its applicability encompasses a wide variety of microstructure-sensitive design problems.