Li
x
La(1–x)/3NbO3 (LLNO) is an A-site-deficient perovskite
material that has a larger unit cell volume, a lower La3+ concentration, and a higher intrinsic vacancy concentration than
(Li
x
La(2–x)/3TiO3), which is known to be one of the fastest
Li-ion conductive oxides. These advantages make LLNO a potential oxide-based
solid electrolyte candidate for all-solid-state Li-ion batteries.
The A-site and B-site elements in this perovskite-type material can
be substituted by ions with various charges and radii in a wide range
of ways to form complicated solid solutions; hence, this type of material
can be adapted to a variety of application requirements. Doping with
monovalent or divalent metal compounds is a promising method for improving
the ionic conductance of this perovskite-type material. In this study,
the (Li
y
La(1–y)/3)1–x
Sr0.5x
NbO3 (0 ≤ 0.5 x ≤ 0.15, 0 ≤ y ≤ 0.3) composition
formed by co-doping with Li2CO3 and SrCO3 was optimized using an exhaustive experimental approach.
Sixty-four samples with different compositions were structurally analyzed,
and their electrochemical performance was experimentally characterized,
which revealed that the co-doped samples have higher ionic conductivities
and superior sintered morphologies compared to those prepared by single
doping. Because Li+ and Sr2+ doping improves
the ionic conductivity for different reasons, and many factors, such
as higher carrier concentrations, enhancements through sintering,
and changes in the microstructure, play important roles, it is difficult
or inefficient to determine the best composition using only traditional
trial-and-error or intuitive searching. Instead, as a proof-of-concept
study, we show that the Bayesian optimization (BO) method efficiently
searches for the best composition and that material retrieval during
experimental exploration can benefit from BO because it significantly
reduces the high workload associated with the trial-and-error approach
employed by the materials industry.