Advanced Machine Learning for EELS Spectroscopy: Magnetic Characterization, Classification and Data Generation

[eng] This PhD thesis explores the use of Machine Learning (ML) techniques to develop advanced methods for the analysis of Electron Energy Loss Spectroscopy (EELS) spectra. EELS is mainly used in Scanning Transmission Electron Microscopy (STEM) to measure the energy loss by electrons as they pass through a thin sample (typically a few tens of nanometers thick). This energy loss provides valuable information about the composition, chemical bonding, and electronic structure of the sample being studied. However, the complexity and large volume of data generated by EELS make manual analysis time-consuming and inaccurate. To address these challenges, this thesis proposes the integration of ML techniques with EELS data to automate and improve the analysis. First, the thesis provides a comprehensive review of ML applications in EELS, analyzing supervised and unsupervised methods. Following this, the study employs Electron Magnetic Circular Dichroism (EMCD) to characterize bi-magnetic nanocubes with a core/shell (FeO/Fe3O4) structure. By combining EMCD with unsupervised ML, particularly K-means clustering, we identified magnetic regions and found a correlation between the structural and magnetic properties of the material. This analysis revealed an onion-like concentric structure in the nanocubes, with a decreasing magnetic moment from the surface to the core, linked to oxidation gradients and composition changes. These results demonstrate that combining the compositional mapping of EELS with EMCD gives valuable understanding of the magnetic interfaces in nanomaterials. Next, ML is applied, specifically, the soft-margin Support Vector Machine (SVM), to classify the oxidation states of transition metals such as iron and manganese. These metals have characteristic EELS features known as white lines, which are sensitive to the oxidation state. By training SVM models on these spectral features, an accurate classification of the oxidation states was achieved. This analysis also highlights the challenges posed by noise and energy shifts in the data, which can complicate classification. To address these issues, this thesis proposes techniques to reduce their impact, improving the robustness of the classification models. In addition to SVMs, this thesis also investigates the application of Artificial Neural Networks (ANNs) to classify oxidation states in EELS spectra. By directly comparing the performance of SVMs and ANNs, the study emphasizes the strengths and weaknesses