Lithium aluminum titanium phosphate (LATP) is known to have a high Li-ion conductivity and is therefore a potential candidate as a solid electrolyte. Via sol-gel route, it is already possible to prepare the material at laboratory scale in high purity and with a maximum Li-ion conductivity in the order of 1·10−3 s/cm at room temperature. However, for potential use in a commercial, battery-cell upscaling of the synthesis is required. As a first step towards sthis goal, we investigated whether the sol-gel route is tolerant against possible deviations in the concentration of the precursors. In order to establish a possible process window for sintering, the temperature interval from 800 °C to 1100 °C and holding times of 10 to 480 min were evaluated. The resulting phase compositions and crystal structures were examined by X-ray diffraction. Impedance spectroscopy was performed to determine the electrical properties. The microstructure of sintered pellets was analyzed by scanning electron microscopy and correlated to both density and ionic conductivity. It is shown that the initial concentration of the precursors strongly influences the formation of secondary phases like AlPO4 and LiTiOPO4, which in turn have an influence on ionic conductivity, densification behavior, and microstructure evolution.

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Lithium-ion batteries with solid electrolytes offer safety, higher energy density and higher long-term performance, which are promising alternatives to conventional liquid electrolyte batteries. Lithium aluminum titanium phosphate (LATP) is one potential solid electrolyte candidate due to its high Li-ion conductivity. To evaluate its performance, influences of the experimental factors on the materials design need to be investigated systematically. In this work, a materials design strategy based on machine learning (ML) is employed to design experimental conditions for the synthesis of LATP. In the variation of parameters, we focus on the tolerance against the possible deviations in the concentration of the precursors, as well as the influence of sintering temperature and holding time. Specifically, models built with different design selection strategies are compared based on the training data assembled from previous laboratory experiments. The best one is then chosen to design new experiment parameters, followed by measuring the corresponding properties of the newly synthesized samples. A previously unknown sample with ionic conductivity of 1.09 × 10−3 S cm−1 is discovered within several iterations. In order to further understand the mechanisms governing the high ionic conductivity of these samples, the resulting phase compositions and crystal structures are studied with X-ray diffraction, while the microstructures of sintered pellets are investigated by scanning electron microscopy. Our studies demonstrate the advantages of applying machine learning in designing experimental conditions by the synthesis of desired materials, which can effectively help researchers to reduce the number of required experiments.