Yokoyama et al., Supplementary Data for "Prediction of Li-ion Conductivity in Ca and Si co-doped LiZr₂(PO₄)₃ Using a Denoising Autoencoder for Experimental Data"

<b>uniDAE.py </b>is the python script used in this study. The script includes a denoising autoencoder for XRD profiles with six attention heads and a deep learning model for regression analysis of the activation energy (Ea) of ion conduction.<b>bestmodel.pth</b> is the trained deep learning model for uniDAE.py used in this study.<b>Input folder</b> contains csv files that is training/validation/test data used in uniDAE.py. (LCZSP_data_with_Ea.csv includes sample number, heating condition, and Ea data. LCZSP_XRD_original_withH.csv contains the same information as <b>LCZSP_data.zip</b> (the Ea values at each sintering temperature). It is formatted for input into the machine learning program.<b>LCZSP_data.zip </b>(contains the same information of LCZSP_XRD_original_withH.csv)Experimental measurement data for the lithium-ion conductor Li1.45Ca0.15Zr1.85Si0.15P2.85O12 (LCZSP) material synthesized using 54 different sintering programs.<br><b>Reference</b> Yokoyama et al. DOI: 10.1063/5.0231411For information on sample synthesis (LCZSP_data.zip), refer to:<br>H. Takeda, H. Fukuda, K. Nakano, S. Hashimura, N. Tanibata, M. Nakayama, Y. Ono, T. Natori, "Process optimisation for NASICON-type solid electrolyte synthesis using a combination of experiments and Bayesian optimisation", Mater. Adv., 3, 8141-8148. (2022) DOI: 10.1039/D2MA00731B.<br><br>