Data for The Persistence of Memory in Ionic Conduction Probed by Nonlinear Optics

Experimental and computational data and analysis workflows for the manuscript "The Persistence of Memory in Ionic Conduction Probed by Nonlinear Optics" (doi:10.1038/s41586-023-06827-6). Python scripts: paper_tke_plots_pub.py is for experimental TKE plots analysis_pumping_pub.py is for computational TKE plots Python requirements for the work-up of experimental data are the typical scientific python stack: numpy, matplotlib, scipy, pandas, sympy. Computational counterpart of the TKE experiment uses essentially the same hopping analysis as our computational study https://www.nature.com/articles/s41563-022-01316-z with its scripting available at https://github.com/apoletayev/anomalous_ion_conduction/ . The python package requirements are, in addition to above, networkx, freud, deepgraph, fastparquet, pyarrow. All python scripts work best when run in a notebook-like fashion cell by cell (e.g. with spyder).Experimental data: TKE, OKE, THz transmission.Computational data: example simulations of Na beta-alumina, K beta-alumina, K beta"-alumina. The files include tracking the simulation temperatures, centers of mass of the mobile ions, and hopping. Basic usage: download and unzip data. Install python dependencies (e.g. using conda or pip). Run python from the same directory in which the data folders are located. All paths in the scripts are relative.