Bayesian-optimization-guided experimental search of NASICON-type solid electrolytes for all-solid-state Li-ion batteries

Abstract

Divalent- or trivalent-metal-oxide-doped NASICON-type LiZr₂(PO₄)₃ (LZP) is an attractive oxide-based solid electrolyte for all-solid-state Li-ion batteries owing to its high Li-ion conductivity and stable charge–discharge cycling. Using an extensive experimental approach, the composition of CaO- and Y₂O₃-co-doped Li-rich NASICON-type LZP of the composition Li_1+x+2yZ_r2−x−yY_xCa_y(PO₄)₃ (0 ≦ x ≦ 0.376, 0≦ y ≦ 0.376) was optimized. A number of 49 compositions were synthesized and their crystal structures, relative density of the sintered material, and Li-ion conductivity characterized. The co-doped compositions had a higher Li-ion conductivity than those doped with CaO or Y₂O₃ alone. Furthermore, diffraction studies revealed that two phases and an impurity phase existed depending on the composition. Adding CaO significantly changed the relative density of the sintered material. Thus, to identify compositional dependencies among the Li-ion conductivity, phase stability, and relative density was complicated, and intuitively searching for the best composition was difficult. As a proof-of-concept, the collected data were used to demonstrate that the Bayesian optimization (BO) significantly improved the experimental search for the best oxide-based electrolyte composition. Multi-objective BO (MOBO), which considers more than two target performances, was also carried out. It was validated that the BO-guided experimental search determined more rapidly the best composition compared to conventional trial-and-error approaches employed in the materials industry.