Stochastic characterization and reconstruction of material microstructures for establishment of process-structure-property linkage using the deep generative model

In material design, microstructure characterization and reconstruction are indispensable for understanding the role of a structure in a process-structure-property relation. The significant contribution of this paper is to introduce a methodology for the characterization and generation of material microstructures using deep generative networks as the first step in the establishment of a process-structure-property linkage for forward or inverse material design. Our approach can be divided into two parts: (i) characterization of material microstructures by a vector quantized variational auto-encoder, and (ii) determination of the correlation between the extracted microstructure characterizations and the given conditions, such as processing parameters and/or material properties, by a pixel convolutional neural network. As an example, we tested our framework in the generation of low-carbon-steel microstructures from the given material processing. The results were in satisfactory agreement with the experimental observation qualitatively and quantitatively, demonstrating the potential of applying the proposed method to forward or inverse material design. One of the advantages of the proposed methodology lies in the capability to capture the stochastic nature behind the microstructure generation. As a result, this methodology enables us to build a process-structure-property linkage while quantifying uncertainties, which not only makes a prediction more robust but also shows a way toward enhancing our understanding of the stochastic competitive phenomena behind the generation of material microstructures.