Identifying the impact of chemical functional groups on ionic liquid conductivity

Area of Science:

• Materials Science
• Computational Chemistry
• Electrochemistry

Background:

• Ionic liquids are promising electrolytes for next-generation batteries due to their stability and non-flammability.
• Predictive design of ionic liquid electrolytes is hindered by strong ion correlations and limitations of standard cheminformatics tools in capturing intermolecular interactions.

Purpose of the Study:

• To develop a novel molecular representation for ionic liquids that explicitly accounts for electrostatic interactions.
• To improve the predictive modeling of ionic liquid conductivity and understand structure-property relationships.

Main Methods:

• Utilized SMARTS molecular substructure searching to create a molecular fragment representation capturing charge-carrier resonance.
• Employed machine learning models and principal covariates regression for predictive analysis and visualization of structure-property relationships.

Main Results:

• The new representation simplifies structure-conductivity relationships and enhances predictive accuracy, particularly in low-data scenarios.
• Identified charge delocalization and anion flexibility as key factors for improved ion transport.
• Found that polar, alkyl, and fluorinated functionalizations generally decrease ion transport due to increased intermolecular interactions.

Conclusions:

• The developed interpretable approach facilitates the design of ionic liquids with tailored transport properties.
• Anion functionalization and cation charge centers are crucial for tuning intermolecular interactions and ion mobility.
• This work provides open-source tools for advancing ionic liquid design for battery applications.