CALiSol-23: Experimental electrolyte conductivity data for various Li-salts and solvent combinations.

Area of Science:

• Materials Science
• Electrochemistry
• Data Science

Background:

• High-performance lithium-ion batteries (LIBs) depend on efficient ion transport in non-aqueous electrolytes.
• Discovering optimal electrolyte compositions requires extensive, time-consuming experimentation.
• A critical need exists for comprehensive datasets to enable data-driven approaches in electrolyte design.

Purpose of the Study:

• To curate and analyze a large-scale dataset of experimentally measured ionic conductivities for non-aqueous electrolytes.
• To support the development of chemistry-agnostic machine learning models for predicting electrolyte conductivity.
• To accelerate the discovery and optimization of novel electrolyte formulations for LIBs.

Main Methods:

• Conducted an exhaustive literature review to collect experimental data on non-aqueous electrolyte conductivity.
• Compiled a dataset, named CALiSol-23, comprising 13,825 data points from 27 research articles.
• The dataset covers 38 different solvents, 14 lithium salts, and a wide temperature range.

Main Results:

• The CALiSol-23 dataset provides a comprehensive resource for ionic conductivity across diverse electrolyte systems.
• It includes detailed information on lithium salts, solvents, concentrations, and temperature variations.
• The dataset facilitates the analysis of ion transport mechanisms and electrolyte performance factors.

Conclusions:

• CALiSol-23 addresses the lack of comprehensive datasets crucial for machine learning-based electrolyte discovery.
• This resource can significantly expedite the development of predictive models for electrolyte conductivity.
• It will streamline the optimization of non-aqueous electrolyte mixtures for advanced energy storage applications.