Authors: Yumika Yokoyama | Naoto Tanihata | Hayami Takeda | Masanobu Nakayama
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Prediction of Li Ion Conductivity Using a Denoising Autoencoder and an Attention for Experimental Data While all-solid-state batteries composed of inorganic materials are attractive in terms of safety, rate performance is still insufficient. One of the causes is rather poor ionic conductivity of the solid electrolytes, and new high ionic conductive materials are required. Li containing NASICON-type materials are one of candidates due to its high Li ion conductivity. Many efforts have been devoted to improve their Li ion conductivities by changing composition and/or sintering conditions. Meantime, many experimental data, such as optical spectroscopy, diffraction, and so forth, are accompanied in the process of measurements for ionic conductivities. Making a link between ionic conductivity and all the analytical outputs may lead to good guideline for designing materials, though interpretation of analytical output is often difficult. In this study, we focused on Li 1.45 Ca 0.15 Zr 1.85 Si 015 P 2.85 O 12 ( LCZSP ) [2] doped with Ca and Si in LiZr 2 (PO4) 3 , which belongs to NASICON-type solid electrolyte that exhibits high lithium-ion conductivity and stability against metallic lithium [1] . We reported that the conductivities of this material change depending on the sintering conditions, where we performed 54 synthesis, XRD and Li ion conductivity measurements. [3] Revisiting these experimental data, we tried to make link between XRD and conductivities through machine learning. Fig. 1 is the schematic representation of the present machine learning model based on deep neural network techniques. An XRD pattern contains noise derived from device, human factors, and so forth. To reduce the influence of noise in the XRD measurement data, a denoising autoencoder was utilized in the model. In addition, XRD patterns are reduced to a two-dimensional representation by the denoising autoencoder and make a link from encoded variables to the ionic conductivities( The blue and yellow areas in Fig. 1 ). The use of the denoising autoencoder enables the construction of a more general model for experimental data. Furthermore, we aimed to enhance the prediction accuracy and interpretability by introducing an attention mechanism that learns the points of interest in the spectrum ( The yellow-green area in Fig. 1 ). Here, we successfully fit the ionic conductivity through autoencoder derived variables, and confirm an improvement of fitting results by implementation of the attention mechanism. The details will be presented in our poster session. References [1] Y. Li, et al. Proc. Natl. Acad. Sci. U. S. A. , 113 ,13313–13317 (2016) [2] F. Hiroko, et al ., RSC Advanced , 12 , 30696-30703 (2022) [3] H. Takeda, et al. , Mater. Adv. , 3 , 8141-8148 (2022) Figure 1 Conductivity ; Ionic conductivity ; Fast ion conductor ; Materials science ; Lithium (medication) ; Electrolyte ; Ion ; Ionic bonding ; Analytical Chemistry (journal) ; Chemistry ; Electrode ; Physical chemistry Machine Learning in Materials Science