Nanoscale structures in battery cathodes from theory, ML, and microscopy/spectroscopy experiments

The determination of nanoscale structural evolution in battery materials during synthesis and cycling is of importance in order to understand battery performance and degradation. The integrated use of first principles density functional theory (DFT) modeling, machine learning (ML), together with microscopy (e.g. STEM), diffraction/scattering, and spectroscopy (e.g. XANES and EELS) measurements, has enabled more in depth understanding of such structural evolution. In this talk, we will discuss how this combination of techniques has allowed us to determine oxygen instability and reactivity, map local cation and defect concentrations, and determine intermediate phases in lithium battery cathode materials.