Berlin 2024 – scientific programme

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MM: Fachverband Metall- und Materialphysik

MM 53: Data Driven Material Science: Big Data and Workflows VI

MM 53.8: Talk

Thursday, March 21, 2024, 12:15–12:30, C 243

Bayesian Optimization for High-Resolution Transmission Electron Microscopy — •Xiankang Tang, Yixuan Zhang, and Hongbin Zhang — Institute of Materials Science, TU Darmstadt, 64287 Darmstadt, Germany

High-resolution transmission electron microscopy (HRTEM) allows to study the atomic structure of solid materials with a resolution of sub-Angstrom. By matching experimental and simulated images, unknown experimental parameters and crystal structures can be determined. However, this process entails strong domain expertise and can be time consuming. In this work, we implement and apply a Bayesian optimization-based approach to improve the efficiency of the image matching process. After adopting properly defined loss functions capturing both the global and local image features, it is demonstrated that our approach can not only match the experimental and simulated images in terms of absolute image contrast, but also naturally identify the unknown parameters in the experiments. This method offers a new possibility for automated HRTEM image analysis.We acknowledge Dr. Lei Jin and Prof. Rafal Dunin-Borkowski from FZ Jülich for stimulating discussions.

Keywords: High-resolution transmission electron microscopy; Bayesian Optimization