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The performance of Li metal batteries is tightly coupled to the composition and properties of the solid electrolyte interphase (SEI). Even though the role of the SEI in battery function is well understood (e.g., it must be electronically insulating and ionically conductive, it must enable uniform Li+ flux to the electrode to prevent filament growth, it must accommodate the large volume changes of Li electrodeposition), the challenges associated with probing this delicate composite layer have hindered the development of Li metal batteries for practical applications. In this review, we detail how nuclear magnetic resonance (NMR) spectroscopy can help bridge this gap in characterization due to its unique ability to describe local structure (e.g., changes in crystallite size and amorphous species in the SEI) in conjunction with ion dynamics while connecting these properties to electrochemical behavior. By leveraging NMR, we can gain molecular-level insight to aid in the design of Li surfaces and enable reactive anodes for next-generation, high-energy-density batteries. 

Abstract Characterization of material structure with X-ray or neutron scattering using e.g. Pair Distribution Function (PDF) analysis most often rely on refining a structure model against an experimental dataset. However, identifying a suitable model is often a bottleneck. Recently, automated approaches have made it possible to test thousands of models for each dataset, but these methods are computationally expensive and analysing the output, i.e. extracting structural information from the resulting fits in a meaningful way, is challenging. OurMachineLearning basedMotifExtractor (ML-MotEx) trains an ML algorithm on thousands of fits, and uses SHAP (SHapley Additive exPlanation) values to identify which model features are important for the fit quality. We use the method for 4 different chemical systems, including disordered nanomaterials and clusters. ML-MotEx opens for a type of modelling where each feature in a model is assigned an importance value for the fit quality based on explainable ML. 

Abstract Using Pair Distribution Function (PDF) analysis of in situ total scattering data, we investigate the formation of tungsten and niobium oxides in a simple solvothermal synthesis. We use Pearson Correlation Coefficient (PCC) analysis of the time resolved PDFs to both map the structural changes taking place throughout the synthesis and identify structural models for precursor and product through PCC‐based structure mining. Our analysis first shows that ultra‐small tungsten and niobium oxide nanoparticles form instantaneously upon heating, with sizes between 1.5 and 2 nm. We show that the main structural motifs in the nanoparticles can be described with structures containing pentagonal columns, which is characteristic for many bulk tungsten and niobium oxides. We furthermore elucidate the structure of the precursor complex as clusters of octahedra with O‐ and Cl‐ligands. The PCC based methodology automates the structure characterization and proves useful for analysis of large datasets of for example, time resolved X‐ray scattering studies. The PCC is implemented in ‘PDF in the cloud’, a web platform for PDF analysis.