Ionic Liquids: A Simple Model to Predict Ion Conductivity Based on DFT Derived Physical Parameters.

Area of Science:

• Materials Science
• Computational Chemistry
• Electrochemistry

Background:

• Predicting ionic liquid (IL) conductivity is crucial for designing new materials.
• Existing models often require experimental parametrization, limiting their a priori predictive power.

Purpose of the Study:

• To propose simple, non-experimental models for predicting IL conductivity.
• To enable rapid screening of novel IL designs using readily available computational data.

Main Methods:

• Developed conductivity models using ion volume, mass, moment of inertia, and ion-ion interaction strength from DFT calculations.
• Models were fitted using data from 22 ILs (12 cations, 5 anions).
• No experimental parametrization was required.

Main Results:

• Achieved high correlation with experimental data (R² ≈ 0.95).
• Reported mean absolute errors (MAE) between 25-36%.
• Demonstrated the models' speed and reusability for IL screening.

Conclusions:

• The proposed simple models offer a fast and efficient tool for a priori prediction of IL conductivity.
• These models facilitate the screening of new IL candidates without experimental fitting.
• The approach provides a valuable design tool for materials scientists and chemists.