Data-driven lithium-ion battery cathode research with state-of-the-art synchrotron X-ray techniques


In this Account, we focus on showcasing the integration of synchrotron and ML techniques for LIB cathode research. We review our recent findings on charge–lattice–morphology–kinetics in LIB cathode materials via this approach. First, the ML-based morphological study of cathode materials is discussed, highlighting a ML-assisted automatic feature recognition, particle identification, and statistical analysis of the prolonged cycling-induced particle damage and detachment from the carbon matrix. Second, we discuss the chemical heterogeneity and lattice deformation in cathode materials revealed by ML-assisted multimodal synchrotron characterizations. The role of ML tools in identifying and understanding chemical outliers and lattice defects in NCM cathodes is highlighted. Third, we provide our perspective on a future “dream” experiment for investigating the spatial distribution of cation–anion redox coupling effects in the battery cathode by means of resonant inelastic X-ray scattering (RIXS) imaging with ML. We anticipate that this new approach will provide new horizons for the development of novel high-energy and high-power-density LIB cathode materials.

Acc. Mater. Res., 2022