Microstructure estimation by combining deep learning and phase transformation model

Abstract

In material design, the establishment of process–structure–property relationship is crucial for analyzing and controlling material microstructures. For the establishment of process–structure–property relationship, a central problem is the analysis, characterization, and control of microstructures, since microstructures are highly sensitive to material processing and critically affect material's properties. Therefore, accurately estimating the morphology of material microstructures plays a significant role in understanding the process–structure–property relationship. In this paper, we propose a deep-learning framework for estimating material microstructures under specific process conditions. The framework utilizes two deep learning networks: vector quantized variational autoencoder (VQVAE) and pixel convolutional neural network (PixelCNN). The framework can predict material microstructures from the transformation behavior given by some physical models. In this sense, the framework is consistent with the physical knowledge accumulated in the field of material science. Importantly, our study demonstrates qualitative and quantitative evidence that incorporating physical models enhances the accuracy of microstructure prediction by deep learning models. These results highlight the importance of appropriately integrating field-specific knowledge when applying data-driven frameworks to materials design. Consequently, our results provide a basis for integrating data-driven methods with the accumulated knowledge in the field. This integration holds great potential for advancing material design through deep learning.