Speaker
Description
Characterizing crystallographic interfaces in synthetic nanomaterials is an important step for the design of novel materials. Trained materials scientists can assign interface structures of materials by looking at high-resolution imaging and diffraction data obtained by aberration-corrected scanning transmission electron microscopy (STEM). However, STEM datasets cannot be fully exploited due to the lack of automatic analysis tools. Here, we present AI-STEM, a newly developed AI tool, based on a a Bayesian neural network, for accurately extracting the key features of (poly)crystalline materials from atomic-resolution STEM images. It achieves excellent predictive performance for identifying crystal structure and lattice misorientations on experimental images.
