Building sensible processing-structure-property (PSP) links to gain fundamental insights and
understanding of materials behavior has been the focus of many works in computational …

This review highlights recent advances in machine learning (ML)‐assisted design of energy
materials. Initially, ML algorithms were successfully applied to screen materials databases …

Microstructure characterization and reconstruction have become indispensable parts of
computational materials science. The main contribution of this paper is to introduce a …

In designing a microstructural materials system, there are several key questions associated
with design representation, design evaluation, and design synthesis: how to quantitatively …

In designing microstructural materials systems, one of the key research questions is how to
represent the microstructural design space quantitatively using a descriptor set that is …

The need for computational characterization and reconstruction of volumetric maps of
stochastic microstructures for understanding the role of material structure in the processing …

Thin fiber networks are widely represented in nature and can be found in man-made
materials such as paper and packaging. The strength of such materials is an intricate subject …

Effective and accurate characterization and quantification of complex microstructure of a
heterogeneous material and its evolution under external stimuli are very challenging, yet …

Accurately quantifying the microstructure of a heterogeneous material is crucial to
establishing quantitative structure-property relations for material optimization and design …

In engineering design, complex systems that involve a multitude of decision variables and
parameters are often decomposed into several submodels (also called subsystems and/or …