We report a solid-state Li-ion electrolyte predicted to exhibit simultaneously fast ionic conductivity, wide electrochemical stability, low cost, and low mass density. We report exceptional density functional theory (DFT)-based room-temperature single-crystal ionic conductivity values for two phases within the crystalline lithium–boron–sulfur (Li–B–S) system: 62 (+9, −2) mS cm–1 in Li5B7S13 and 80 (−56, −41) mS cm–1 in Li9B19S33. We report significant ionic conductivity values for two additional phases: between 0.0056 and 0.16 mS/cm –1 in Li2B2S5 and between 0.0031 and 9.7 mS cm–1 in Li3BS3 depending on the room-temperature extrapolation scheme used. To our knowledge, our prediction gives Li9B19S33 and Li5B7S13 the second and third highest reported DFT-computed single-crystal ionic conductivities of any crystalline material. We compute the thermodynamic electrochemical stability window widths of these materials to be 0.50 V for Li5B7S13, 0.16 V for Li2B2S5, 0.45 V for Li3BS3, and 0.60 V for Li9B19S33. Individually, these materials exhibit similar or better ionic conductivity and electrochemical stability than the best-known sulfide-based solid-state Li-ion electrolyte materials, including Li10GeP2S12 (LGPS). However, we predict that electrolyte materials synthesized from a range of compositions in the Li–B–S system may exhibit even wider thermodynamic electrochemical stability windows of 0.63 V and possibly as high as 3 V or greater. The Li–B–S system also has a low elemental cost of approximately 0.05 USD/m2 per 10 μm thickness, which is significantly lower than that of germanium-containing LGPS, and a comparable mass density below 2 g/cm3. These fast-conducting phases were initially brought to our attention by a machine learning-based approach to screen over 12,000 solid electrolyte candidates, and the evidence provided here represents an inspiring success for this model.

Machine learning (ML)‐based approaches to battery design are relatively new but demonstrate significant promise for accelerating the timeline for new materials discovery, process optimization, and cell lifetime prediction. Battery modeling represents an interesting and unconventional application area for ML, as datasets are often small but some degree of physical understanding of the underlying processes may exist. This review article provides discussion and analysis of several important and increasingly common questions: how ML‐based battery modeling works, how much data are required, how to judge model performance, and recommendations for building models in the small data regime. This article begins with an introduction to ML in general, highlighting several important concepts for small data applications. Previous ionic conductivity modeling efforts are discussed in depth as a case study to illustrate these modeling concepts. Finally, an overview of modeling efforts in major areas of battery design is provided and several areas for promising future efforts are identified, within the context of typical small data constraints.

High-performance, practical all-solid-state batteries (ASSBs) require solid-state electrolytes (SSEs) with fast Li-ion conduction, wide electrochemical stability window, low cost, and low mass density. Recent density functional theory (DFT) simulations have suggested that lithium thioborates are a particularly promising class of materials for high-performance SSEs in Li batteries, but these materials have not been studied extensively experimentally due to synthesis difficulty. Particularly, their electrochemical properties remain largely underexplored, limiting their further development and application as SSEs. In this work, we report the successful synthesis and a comprehensive electrochemical performance study of single-phase, crystalline Li6+2x [B10S18]­S x (x ≈ 1). We find cold-pressed samples of Li6+2x [B10S18]­S x (x ≈ 1) to exhibit a high ionic conductivity of 1.3 × 10–4 S cm–1 at room temperature. Furthermore, Li6+2x [B10S18]­S x (x ≈ 1) shows an electrochemical stability window of 1.3–2.5 V, much wider than most sulfide SSEs. Symmetrical Li–Li cells fabricated with a Li6+2x [B10S18]­S x (x ≈ 1) pellet were cycled up to a current density of 1 mA cm–2 and exhibited good long-term cycling stability for more than 140 h at 0.3 mA cm–2. These results suggest Li6+2x [B10S18]­S x (x ≈ 1) as a promising choice of SSE for high-performance ASSBs for energy storage.

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