Quantifying the search for solid Li-ion electrolyte materials by anion: a data-driven perspective

Abstract: We compile data and machine learned models of solid Li-ion electrolyte performance to assess the state of materials discovery efforts and build new insights for future efforts. Candidate electrolyte materials must satisfy several requirements, chief among them fast ionic conductivity and robust electrochemical stability. Considering these two requirements, we find new evidence to suggest that optimization of the sulfides for fast ionic conductivity and wide electrochemical stability may be more likely than optimization of the oxides, and that the oft-overlooked chlorides and bromides may be particularly promising families for Li-ion electrolytes. We also find that the nitrides and phosphides appear to be the most promising material families for electrolytes stable against Li-metal anodes. Furthermore, the spread of the existing data in performance space suggests that fast conducting materials that are stable against both Li metal and a >4V cathode are exceedingly rare, and that a multiple-electrolyte architecture is a more likely path to successfully realizing a solid-state Li metal battery by approximately an order of magnitude or more. Our model is validated by its reproduction of well-known trends that have emerged from the limited existing data in recent years, namely that the electronegativity of the lattice anion correlates with ionic conductivity and electrochemical stability. In this work, we leverage the existing data to make solid electrolyte performance trends quantitative for the first time, building a roadmap to complement material discovery efforts around desired material performance.