Visualization of SEM/EBSD Features of Ionic Conductivity with Convolutional Neural Networks

Abstract

To automatically extract the features required to predict the material properties from microstructure, we utilized convolutional newral networks (CNNs). Training of CNNs was carried out by using experimentally obtained scanning electron microscope images and ionic conductivities. After training, the nodes, which placed after global average pooling (GAP), specically activated by passing through the images with high/low ionic conductivities were searched. Then, the corresponding feature maps just before GAP operations were shown. We found that the CNNs focused on the reagion containing large voids and on the area without any crystal defects for the specic features for the material with high ionic conductivities while on the small voids for low ionic conductivities. Such observations agree with knowledge of materials engineering.