Featurization Approaches for Machine Learning of X-ray Absorption Spectra

Machine learning (ML) has been accelerating the analysis and interpretation of materials characterization data. For example, it has been utilized to extract essential material properties such as oxidation states and structural information from X-ray absorption spectroscopy (XAS). While most ML models focus on the raw spectra intensities as the model input, transformation of spectra that can potentially enhance model performance remains scarcely explored. In this presentation, we will benchmark both the reduced dimension features and overcomplete representation to unveil the optimal representation of spectroscopy data. The system of interest is a cathode material for Li-ion battery, LiNi_xMn_yCo_zO₂ (NMC). Despite its high energy density, excellent long-term cyclability and relatively low economic cost, the transition metal mixing makes it challenging to investigate the detailed changes during electrochemical cycling. The performance of these input transformations will be assessed for XAS based on both regression and classification tasks. We will demonstrate that such featurization can significantly improve not only the prediction accuracy, but also the interpretability of ML models. Model validation on unseen experimental dataset will also be discussed to prove the model transferability. On the other hand, despite that defect of NMC materials is well-observed during experiment, less emphasis has placed on its formation mechanism and impact on battery performance. In this project, we will also aim to tackle the formation mechanism of those defects through a combination of first-principles calculations and ML techniques.