Decoding Spatial Symmetry and EELS Spectroscopic Fine Structures.

Decoding spatial symmetry in TEM images and fine structure in EELS spectra using deep learning is a challenging task that requires addressing several key issues.One of the main challenges is the lack of labeled data for training deep learning models to detect, classify, and regress different types of spatial symmetry and near-edge fine structures of different bonding environments.This can make it difficult to develop accurate and robust models that can generalize well to new data.Another challenge is selecting the correct network architecture for the specific task at hand.In this talk, I will discuss methods for generating high-quality labeled data and leveraging the latest advances in natural language processing for spatial and spectral data.I will review recent research in this area [1][2][3] and offer insights into how to overcome these challenges to develop accurate and robust deep learning models for decoding spatial symmetry and fine structure in EELS spectra [4].