Authors: Hayami Takeda | Hiroko Fukuda | K. Nakano | Naoto Tanihata | Masanobu Nakayama | Takaaki Natori | Yasuharu Ono
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Development of Nasicon-Type Electrolytes Using Combination of Experimental Techniques and Materials Informatics Introduction Research and development of large lithium-ion batteries is essential for new societal infrastructures such as electric vehicles, aircraft and smart grids. Replacing the liquid electrolyte of conventional lithium-ion batteries with solid electrolytes not only makes them less prone to ignition, but also increases their energy density. Sulfide, chloride and oxide systems are known as solid electrolytes. Sulphide and chloride types have high lithium-ion conductivity but are generally unstable in air. Therefore, there is an urgent need to develop oxide-based solid electrolytes that are stable in air, and the Na superconductor (NASICON) type LiZr 2 (PO 4 ) 3 (LZP) is a candidate oxide-based solid electrolyte for all-solid-state lithium-ion batteries. However, its ionic conductivity is inadequate. We have therefore investigated co-doped LZP (Li 1+x+2y Ca y Zr 2-y Si x P 3-x O 12 ), in which Zr is partially replaced by Ca and P by Si. First, a total of 49 compositions were synthesized and their crystal structure, relative density and lithium-ion conductivity were experimentally evaluated [1]. Since lithium-ion conductivity is also affected by microstructural properties such as density, morphology and elemental distribution of the sintered pellets, the conductivity can be improved by controlling process parameters such as heating conditions during the solid-state reaction. Therefore, the material was synthesized in a solid-state reaction under different heating conditions in two stages and the effect of the heating conditions on the microstructural properties of the sintered pellets was investigated [2].Such thorough sample preparation requires considerable time, effort and cost, and consumes a large amount of energy and resources. Therefore, AI-based optimization methods called Bayesian Optimization (BO) have attracted attention. Bayesian optimizations is a method that uses statistical inference to select the next candidate to explore and find the best performing sample with the least number of samples. Using the experimental data obtained in our study, we demonstrated whether the BO algorithm can 'efficiently' find the optimal composition and heating conditions to achieve the highest ionic conductivity of such materials. Experimental Li 1+x+2y Ca y Zr 2-y Si x P 3-x O 12 was synthesized by a conventional solid-state reaction using two-stage heating. The crystalline phase of the samples was determined by X-ray diffraction (XRD). The micro morphology of the samples was evaluated by scanning electron microscopy. The ionic conductivity of the pellets was measured by AC impedance.BO was performed to evaluate the efficiency of the optimized composition or heating conditions to obtain the highest ionic conductivity of the experimentally evaluated samples. Conductivity at 30 °C was the objective. Results and discussion The Li 1.45 Ca 0.15 Zr 1.85 S 0.15 P 2.75 O 12 sample heated at 1050°C and 1250°C gave the highest lithium-ion conductivity of 3.3 x 10 -5 S/cm at 30°C. Investigation of the sintered samples showed that composition and heating conditions affected the crystalline phase, density, microstructure and elemental distribution. The relationship between ionic conductivity and these observations was so complex that it was difficult to determine the optimum composition and heating conditions intuitively from a few experiments. Figure 1 shows the number of experiments required to find (a) the optimal composition and (b) the optimal heating conditions with 80%, 90% and 100% probability using BO or random search. The results show that using BO can find the optimum composition or optimum sintering conditions in about half the number of trials compared to a random search. Thus, Bayesian optimization has been shown to be effective in finding the optimum composition or process conditions of a material. References [1] H. Fukuda et al., RSC Adv., 12, 30696-30703 (2022) [2] H. Takeda et al., Mater. Adv., 3, 8141-8148. (2022) Figure 1 Fast ion conductor ; Electrolyte ; Materials science ; Computer science ; Chemistry ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Chemical Synthesis and Characterization