The timely arrival of novel materials plays a key role in bringing advances to society, as the pace at which major technological breakthroughs take place is usually dictated by the discovery rate at which novel materials are identified within chemical space. High‐throughput experimentation and computation strategy, now widely considered as a watershed in accelerating the discovery and optimization of novel materials in virtually every field, enables simultaneous screening, synthesis and characterization of large arrays of different material classes toward identification of the lead candidates for given system and targeted application. However, the ability to acquire data, through the continued advancement of automation platforms and workflows especially in the field of battery research and development, often outpaces the ability to optimally leverage obtained data for improved decision‐making. Closing this gap inevitably calls for adapted algorithms, development of reliable predictive models and enhanced integration with machine learning, deep learning, and artificial intelligence. This Review aims to highlight state‐of‐the‐art achievements along with an assessment of current and future challenges as well as resulting perspectives toward accelerated development of advanced battery electrolytes and their interfaces.

Open access to research data is increasingly important for accelerating research. Grant authorities therefore request detailed plans for how data is managed in the projects they finance. We have recently developed such a plan for the EUÀ H2020 BIG-MAP project-a cross-disciplinary project targeting disruptive battery-material discoveries. Essential for reaching the goal is extensive sharing of research data across scales, disciplines and stakeholders, not limited to BIG-MAP and the European BATTERY 2030 + initiative but within the entire battery community. The key challenges faced in developing the data management plan for such a large and complex project were to generate an overview of the enormous amount of data that will be produced, to build an understanding of the data flow within the project and to agree on a roadmap for making all data FAIR (findable, accessible, interoperable, reusable). This paper describes the process we followed and how we structured the plan.

Summary Single-ion conducting polymer electrolytes exhibit great potential for next-generation high-energy-density Li metal batteries, although the lack of sufficient molecular-scale insights into lithium transport mechanisms and reliable understanding of key correlations often limit the scope of modification and design of new materials. Moreover, the sensitivity to small variations of polymer chemical structures (e.g., selection of specific linkages or chemical groups) is often overlooked as potential design parameter. In this study, combined molecular dynamics simulations and experimental investigations reveal molecular-scale correlations among variations in polymer structures and Li + transport capabilities. Based on polyamide-based single-ion conducting quasi-solid polymer electrolytes, it is demonstrated that small modifications of the polymer backbone significantly enhance the Li + transport while governing the resulting membrane morphology. Based on the obtained insights, tailored materials with significantly improved ionic conductivity and excellent electrochemical performance are achieved and their applicability in LFP||Li and NMC||Li cells is successfully demonstrated.