Efficient Experimental Search for Discovering a Fast Li-Ion Conductor from a Perovskite-Type Li_xLa_(1–x)/3NbO₃ (LLNO) Solid-State Electrolyte Using Bayesian Optimization

Abstract

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.

Materials informatics for discovery of ion conductive ceramics for batteries

“…Accordingly, the informatics approach is also useful for large experimental datasets. 6), 28) In the future, efficient optimization is possible, including processing parameters, such as sintering temperature and gas atmosphere, by developing appropriate descriptors.…”

Machine Learning Assisted Design of Experiments for Solid State Electrolyte Lithium Aluminum Titanium Phosphate

“…Admittedly, this is a simplification of the optimization problem, since other properties (e.g., sintered density) can also affect our interested property. As a result, taking them into consideration and regarding it as a multiobjective optimization problem (previous studies concerning similar question can be found in (Harada et al, 2020;Yang et al, 2020)) may further improve the performance of the model and may hence result in better samples. This question deserves to be explored in details and is left for the future research.…”

“…Hence, a series of active learning methods based on Bayesian optimization can be used to find the optimal material composition or to optimize the experimental parameters. This type of method has been successfully applied in different materials system, such as low thermal hysteresis shape memory alloys (Xue et al, 2016), BaTiO 3 -based ceramics with better dielectric energy storage density (Yuan et al, 2019), fast ion conductors for rechargeable batteries (Jalem et al, 2018;Harada et al, 2020;Homma et al, 2020;Yang et al, 2020), oxygen evolution reaction catalyst (Rohr et al, 2020), and organic thin films (MacLeod et al, 2020).…”

Density Functional Theory Studies on Li Metal Electrode/Garnet‐Type Li7La3Zr2O12 Solid Electrolyte Interfaces for Application in All‐Solid‐State Batteries

“…Among them, oxide-based Li-ion conductors are advantageous because of their nonflammable nature, good chemical stability, and mechanical strength. Although several oxide-based compounds, such as β-alumina-type, [17] LISICON, [18,19] NASICON, [20][21][22][23] perovskitetype, [16,[24][25][26] and garnet-type oxides, [27,28] possess fast ionic conductivities, the majority of these compounds react with Li metal. Since the first report by Murugan et al in 2007, [27] garnet-type Li 7 La 3 Zr 2 O 12 (LLZ) compounds have gained increasing attention due to their fast Li-ion conductivity (i.e., >10 À4 S cm À1 at room temperature) and their nonreactivity with molten Li metal.…”