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.Comment: Main text is 41 pages with 3 figures and 2 tables; attached
supplemental information is 8 pages with 3 figure
