Supplementary Information and codes for "Prediction of Ionic Conductivity in Solid-State Electrolytes Using Machine Learning"

Supplementary information (Tables and Figures) and codes for Prediction of Ionic Conductivity in Solid-State Electrolytes Using Machine LearningThis repository contains all datasets, supplementary informaion, and code used in the study. The materials are organized as follows:Ion conductivitiy.ipynb - Jupiter notebook containing the code used for regression modeling.Dataset for Li-ion conductivity - Curated dataset of room-temperature ionic conductivities for Li-based solid electrolytes.Dataset for Na-ion conductivity - Curated dataset of room-temperature ionic conductivities for Li-based solid electrolytes.Dataset for Li and Na-ion conductivity - Combined dataset including both Li- and Na-based materialsDataset for Li and Na-ion conductivity(host materials) - Dataset using host material featurization with one-hot encoding of the mobile ion.Supplementary Information (Tables and Figures) - Additional tables and figures referenced in the manuscript.All datasets were obtained through a combination of large language model-assisted extraction and manual curation. The code provided reproduces the regression and classification models described in the article.

基于机器学习预测固态电解质离子电导率的补充资料（含表格与图表）及配套代码

本仓库包含本研究使用的全部数据集、补充资料与代码。资料具体组织如下：
- Ion_conductivity.ipynb：包含回归建模代码的Jupyter Notebook文件
- 锂离子电导率数据集：经人工筛选整理的锂基固态电解质室温离子电导率数据集
- 钠离子电导率数据集：经人工筛选整理的锂基固态电解质室温离子电导率数据集
- 锂钠离子电导率数据集：包含锂基与钠基材料的整合数据集
- 锂钠离子电导率数据集（主体材料版本）：采用主体材料特征化方法，并对迁移离子进行独热编码的数据集
- 补充资料（表格与图表）：论文手稿中引用的额外表格与图表

所有数据集均通过大语言模型（Large Language Model，LLM）辅助提取结合人工整理的方式获取。本仓库提供的代码可复现论文中所述的回归与分类模型。