Exploring the deviation from Nernst-Einstein conductivity in ionic liquids using machine learning

Key Points

• The aim is to investigate how ionic liquids deviate from expected conductivity models using machine learning techniques.
• Analyzed various ionic liquids for conductivity properties
• Applied machine learning to identify patterns and deviations
• Evaluated the influence of chemical composition on conductivity
• Identified significant deviations from Nernst-Einstein predictions
• Machine learning models improved understanding of ionic liquid behaviors
• Provided insights for potential composition adjustments to enhance conductivity