  This paper puts forward an integrated microstructure design methodology that
replaces the common existing design approaches: 1) reconstruction of
microstructures, 2) analyzing and quantifying material properties, and 3)
inverse design of materials using deep-learned generative and surrogate models.
The long-standing issue of microstructure reconstruction is well addressed in
this study using a new class of state-of-the-art generative model, the
diffusion-based generative model (DGM). Moreover, the conditional formulation
of DGM for guidance to the embedded desired material properties with a
transformer-based attention mechanism enables the inverse design of
multifunctional composites. A convolutional neural network (CNN)-based
surrogate model is utilized to analyze the nonlinear material behavior to
facilitate the prediction of material properties for building
microstructure-property linkages. Combined, these generative and surrogate
models enable large data processing and database construction that is often not
affordable with resource-intensive finite element method (FEM)-based direct
numerical simulation (DNS) and iterative reconstruction methods. An example
case is presented to demonstrate the effectiveness of the proposed approach,
which is designing mechanoluminescence (ML) particulate composites made of
europium and dysprosium ions. The results show that the inversely-designed
multiple ML microstructure candidates with the proposed generative and
surrogate models meet the multiple design requirements (e.g., volume fraction,
elastic constant, and light sensitivity). The evaluation of the generated
samples' quality and the surrogate models' performance using appropriate
metrics are also included. This assessment demonstrates that the proposed
integrated methodology offers an end-to-end solution for practical material
design applications.
