Application of Deep Learning in Materials Design: Extraction of Process-Structure-Property Relationship

Abstract

In material design, the establishment of a process-structure-property linkage is indispensable for developing a general methodology for inverse material design and understanding the physical mechanisms behind material microstructure generation. In recent years, deep learning based methods have received much attention in the field of computational material design. Thus, we developed the general deep learning methodology for extraction of a process-structure-property linkage.Our approach can be divided into two parts: characterization of material microstructures by a vector quantized variational auto-encoder, and 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. In this work, we present the following three our recent results: (i) extraction of the process-structure relationship of structural material by our deep learning framework, (ii) identification of a part of microstructures critically affecting the target property without giving the background physics, and (iii) molecular structure optimization by PixelCNN.