The Role of Deep Learning and High-Throughput Dynamical Simulations in Space Group Determination from Kikuchi Patterns

However, in recent times, our capability to synthesize a large number of materials has outpaced our speed of characterizing them, and thorough crystal structure analysis of newly synthesized samples remains a bottleneck in high-throughput nanomaterials discovery. Thus, rapid methods for crystal symmetry determination that can analyze many material samples within a short time frame are especially needed.

Kikuchi diffraction in the scanning electron microscope (SEM) is a promising technique for this due to its sensitivity to dynamical scattering, which may provide space group information. In this study, we train neural networks to classify the space group from cubic Kikuchi patterns to enable its use for high-throughput symmetry classification of novel material samples. Due to the low number of material phases in the experimental training data, an unsupervised deep learning-based domain adaptation method was utilized to train neural networks on both simulated and experimental data.