The modeling framework employed in this work offer a direct tool to assess charge transfer kinetics at electrode interfaces. This approach lends itself well to rationalization, finetuning, or testing of a limited selection of battery systems where refined understanding is required. However, the large computational overhead of the DFT methods prohibits high-throughput studies, why future work will target development of faster approximative methods to enable screening for improved battery components. In this direction, a few promising descriptors correlating with the 𝜆, 𝜔 𝐿𝑖 +, 𝛽, and 𝐻 DA charge transfer properties are highlighted below: first, 𝜆 is known to correlate with the static and optical dielectric constants of the medium; second, the electrostatic potential (ESP), oxygen/lithium pDOS centers, charge population analysis, and the Crystal Orbital Hamilton Population (COHP) are promising for estimations of 𝛽; and, third, 𝜔 Li + could be evaluated from solvation or ion-pairing energies. Last, estimating HDA is challenging, but positive results based on neural network models have been shown for molecular systems based on atomic orbital overlap and geometric descriptors. In brief, future studies are encouraged to evaluate the herein employed charge-transfer property descriptors 𝜆, 𝜔 Li +, 𝛽, and 𝐻 DA through computationally efficient physics-derived or machine-learning/data-driven surrogate models. In summary, this study introduces and employs a computational workflow based on coupled ion-electron theory