Feature engineering of material structure for AI-based materials knowledge systems

Abstract

This tutorial introduces systematically the foundational concepts undergirding the recently formulated AI (artificial intelligence)-based materials knowledge system (AI-MKS) framework. More specifically, these concepts deal with features engineering the heterogeneous material internal structure to obtain low-dimensional representations that can then be combined with machine learning models to establish low-computational cost surrogate models for capturing the process-structure-property linkages over a hierarchy of material structure/lengths scales. Generally referred to as materials knowledge systems (MKS), this framework synergistically leverages the emergent AI/ML (machine learning) toolsets in conjunction with the modern experimental and physics-based simulation toolsets employed currently by the domain experts in the materials field. The primary goal of this tutorial is to present to the domain expert the foundations needed to understand and take advantage of the impending opportunities arising from a synergistic integration of AI/ML tools into the current materials innovation efforts while identifying a specific path forward for accomplishing this goal.