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Ionic liquid conductivity models by symbolic regression.

Isak Bengtsson1, Patrik Johansson1,2,3

  • 1Department of Physics, Chalmers University of Technology, SE-41296, Göteborg, Sweden. isak.bengtsson@chalmers.se.

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Summary

Ionic liquid electrolytes offer an alternative to traditional lithium-ion battery electrolytes. This study uses symbolic regression and free volume theory to model ionic conductivity, improving electrolyte performance prediction.

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Area of Science:

  • Materials Science
  • Electrochemistry
  • Computational Chemistry

Background:

  • Traditional lithium-ion battery electrolytes face challenges like thermal instability.
  • Ionic liquids (ILs) present a promising alternative electrolyte but their ion transport mechanisms are not fully understood.

Purpose of the Study:

  • To develop a novel model for predicting ionic conductivity in IL-based electrolytes.
  • To derive analytical expressions from free volume theory (FVT) using symbolic regression (SR).

Main Methods:

  • Utilized symbolic regression (SR) to identify analytical expressions based on free volume theory (FVT).
  • Employed molecular descriptors as inputs for model development.
  • Validated models using both in-house experimental data (22 ILs) and a large dataset from literature (338 ILs).

Main Results:

  • Achieved high correlations (R² = 0.97 training, R² = 0.94 validation) on the in-house dataset.
  • Demonstrated appreciable performance on a larger literature dataset (R² = 0.76 training, R² = 0.73 validation).
  • Found that FVT-derived models perform best for ILs with well-dissociated ions and less effectively for those with strong ion-ion interactions.

Conclusions:

  • Symbolic regression combined with free volume theory provides a powerful approach to model ionic conductivity in ILs.
  • Model performance is influenced by the quality and characteristics of the ILs, as well as data variability from diverse literature sources.
  • This work advances the understanding and prediction of ion transport in IL electrolytes for improved battery performance.