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To reduce the cobalt (Co) content in lithium-ion batteries, Ni-rich (high-Ni) lithium nickel manganese cobalt oxides (NMC) are pursued as one of the next-generation cathode materials. However, there is still debate on the crystal and electronic structures of the baseline, LiNiO2. Density Functional Theory (DFT) calculations were performed to provide a theoretical understanding of Ni-rich NMC. First, it was found that the commonly used R m structure for LiNiO2 is metallic, contrary to the experimentally reported mix-conducting behavior. Among the four different space groups, R m, C2/m, P21/c, and P2/c, P2/c with charge disproportionation of Ni2+ and Ni4+ is the most energetically stable and semiconducting structure of LiNiO2. Therefore, the atomic structures of representative Ni-rich NMC were built by partially replacing Ni with Co or Mn in the P2/c LiNiO2 to form LixNiyMnzCo1-y-zO2. In the fully lithiated (x = 1.0) high Ni content NMC (y > 0.5), the oxidation state of all Mn ions becomes 4+, while Co ions still maintain 3+, and part of the Ni ions become 2+ to compensate for the charge. Upon delithiation, the local environment shows more variation of the charge states on the transition metal (TM) ions. The average oxidation on each TM follows a sequence of losing electrons that starts from Ni2+ to Ni3+, then oxidizing Ni3+ and Co3+, while Mn4+ remains electrochemically inactive till x = 0. A general relationship for the oxidation state change in each TM as a function of x and y is derived and shows agreement with both modeling and experimental data. 

In the absence of experimental data of fully developed hierarchical 3D sodium solid-state batteries, we developed an improved continuum model by relying on Machine Learning-assisted parameter fitting to uncover the intrinsic material properties that can be transferred into different battery models. The electrochemical system simulated has sodium metal P2-type Na_2/3[Ni_1/3Fe_1/12Mn_7/12]O₂(NNFMO) as the cathode material, paired with two types of electrolytes viz, the organic liquid electrolyte and a solid polymer electrolyte. We implemented a 1D continuum model in COMSOL to suit both liquid and solid electrolytes, then used a Gaussian Process Regressor to fit and evaluate the electrochemical parameters in both battery systems. To enhance the generalizability of our model, the liquid cell and solid cell models share the same OCV input for the cathode materials. The resulting parameters are well aligned with their physical meaning and literature values. The continuum model is then used to understand the effect of increasing the thickness of the cathode and current density by analyzing the cathode utilization, and the overpotentials arising from transport and charge transfer. This 1D model and the parameter set are ready to be used in a 3D battery architecture design. 

Interface resistance has become a significant bottleneck for solid-state batteries (SSBs). Most studies of interface resistance have focused on extrinsic mechanisms such as interface reactions and imperfect contact between electrodes and solid electrolytes. Interface potentials are an important intrinsic mechanism that is often ignored. Here, we highlight Kelvin probe force microscopy (KPFM) as a tool to image the local potential at interfaces inside SSBs, examining the existing literature and discussing challenges in interpretation. Drawing analogies with electron transport in metal/semiconductor interfaces, we showcase a formalism that predicts intrinsic ionic resistance based on the properties of the contacting phases, and we emphasize that future battery designs should start from material pairs with low intrinsic resistance. We conclude by outlining future directions in the study of interface potentials through both theory and experiment.