The solid electrolyte interphase (SEI) is a complex passivation layer that forms in situ on many battery electrodes such as lithium‐intercalated graphite or lithium metal anodes. Its essential function is to prevent the electrolyte from continuous electrochemical degradation, while simultaneously allowing ions to pass through, thus constituting an electronically insulating, but ionically conducting material. Its properties crucially affect the overall performance and aging of a battery cell. Despite decades of intense research, understanding the SEI's precise formation mechanism, structure, composition, and evolution remains a conundrum. State‐of‐the‐art computational modeling techniques are powerful tools to gain additional insights, although confronted with a trade‐off between accuracy and accessible time‐ and length scales. In this review, it is discussed how recent advances in data‐driven models, especially the development of fast and accurate surrogate simulators and deep generative models, can work with physics‐based and physics‐informed approaches to enable the next generation of breakthroughs in this field. Machine learning‐enhanced multiscale models can provide new pathways to inverse the design of interphases with desired properties.

An in-house, unique, custom-developed high-throughput experimentation facility, used for discovery of novel and optimization of existing electrolyte formulations for diverse cell chemistries and targeted applications, follows a high-throughput formulation-characterization-performance-elucidation-optimization-evaluation chain based on a set of previously established requirements. Here, we propose a scalable data-driven workflow to predict ionic conductivities of non-aqueous battery electrolytes based on linear and Gaussian regression, considering a dataset acquired from one-of-a-kind high-throughput electrolyte formulation to high-throughput conductivity measurement sequence. Deeper insight into various compositional effects is gained from a generalized Arrhenius analysis, in which conductivities, activation energies and deviations from Arrhenius behavior are determined separately. Each observable displays a specific dependence on the electrolyte salt concentration. The conductivity is fully insensitive to the addition of electrolyte additives for otherwise constant molar composition. We also discuss and interpret qualitative trends predicted by the data-driven model in light of physical features such as viscosity or ion association effects.

A specially designed high-throughput experimentation facility, used for the highly effective exploration of electrolyte formulations in composition space for diverse battery chemistries and targeted applications, is presented. It follows a high-throughput formulation-characterization-optimization chain based on a set of previously established electrolyte-related requirements. Here, the facility is used to acquire large dataset of ionic conductivities of non-aqueous battery electrolytes in the conducting saltsolvent/co-solvent-additive composition space. The measured temperature dependence is mapped on three generalized Arrhenius parameters, including deviations from simple acti-vated dynamics. This reduced dataset is thereafter analyzed by a scalable data-driven workflow, based on linear and Gaussian process regression, providing detailed information about the compositional dependence of the conductivity. Complete insensitivity to the addition of electrolyte additives for otherwise constant molar composition is observed. Quantitative dependencies, for example, on the temperature-dependent conducting salt content for optimum conductivity are provided and discussed in light of physical properties such as viscosity and ion association effects.