Lessons Learned on Obtaining Reliable Conductivity Estimates From Molecular Dynamics Simulations

Area of Science:

• Computational Chemistry
• Materials Science
• Electrochemistry

Background:

• Calculating ionic conductivity via molecular dynamics (MD) is crucial for electrolytes.
• The Nernst-Einstein method often fails for strongly correlated systems like ionic liquids.
• Accurate conductivity calculations are essential for designing advanced energy storage materials.

Purpose of the Study:

• Introduce the new 'conduct' module in TRAVIS for MD simulations.
• Enable reliable calculation of ionic conductivity, accounting for ionic correlations.
• Provide best practices for estimating physicochemical properties from electrolyte MD.

Main Methods:

• Implemented Einstein-Helfand and Green-Kubo formalisms in the TRAVIS 'conduct' module.
• Utilized MD simulations to model ionic liquid [EMIm][DCA] and LiFSI/DME electrolyte.
• Compared different methods for statistically reliable property estimation.

Main Results:

• The 'conduct' module successfully calculates ionic conductivity by including ionic correlations.
• Demonstrated the module's capability in simulating complex ionic liquids and electrolytes.
• Provided a framework for assessing transport numbers and the inverse Haven ratio.

Conclusions:

• The TRAVIS 'conduct' module offers a robust approach for accurate ionic conductivity calculation.
• The implemented methods overcome limitations of the Nernst-Einstein approach for correlated systems.
• This tool facilitates reliable characterization of electrolyte properties for energy applications.