Data for the Persistence of Memory in Ionic Conduction Probed by Nonlinear Optics
Zenodo (CERN European Organization for Nuclear Research)(2023)
University of Oxford
Abstract
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.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined