This article describes research at Los Alamos National Laboratory that is aimed at building a coupled experimental and computational methodology that supports the development of predictive damage capabilities by: (1) capturing real distributions of microstructural features from real material and implementing them as digitally generated microstructures in damage model development, and (2) distilling structure-property information to link microstructural details to damage evolution under a multitude of loading states.