Energy Loss Spectroscopy

Electron energy loss spectroscopy (EELS) analyzes materials by measuring the energy loss of electrons passing through a sample, revealing information about its elemental composition and electronic structure. Current research focuses on automating EELS data analysis, particularly core-loss edge recognition, using deep learning techniques like convolutional neural networks and recurrent neural networks (e.g., CNN-BiLSTM) to improve speed and accuracy. This automation is crucial for high-throughput materials characterization, particularly in fields like energy storage and catalysis where detailed analysis of oxidation states is vital, leading to faster and more efficient material discovery and optimization.