Classification of crystal structures using electron diffraction patterns with a deep convolutional neural network

Abstract

Investigations have been made to explore the applicability of an off-the-shelf deep convolutional neural network (DCNN) architecture, residual neural network (ResNet), to the classification of the crystal structure of materials using electron diffraction patterns without prior knowledge of the material systems under consideration. The dataset required for training and validating the ResNet architectures was obtained by the computer simulation of the selected area electron diffraction (SAD) in transmission electron microscopy. Acceleration voltages, zone axes, and camera lengths were used as variables and crystal information format (CIF) files obtained from open crystal data repositories were used as inputs. The cubic crystal system was chosen as a model system and five space groups of 213, 221, 225, 227, and 229 in the cubic system were selected for the test and validation, based on the distinguishability of the SAD patterns. The simulated diffraction patterns were regrouped and labeled from the viewpoint of computer vision, i.e., the way how the neural network recognizes the two-dimensional representation of three-dimensional lattice structure of crystals, for improved training and classification efficiency. Comparison of the various ResNet architectures with varying number of layers demonstrated that the ResNet101 architecture could classify the space groups with the validation accuracy of 92.607%.