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Polarizable force field development and molecular dynamics simulations of ionic liquids
1Wasatch Molecular Inc., 2141 St. Marys Drive, Ste 102, Salt Lake City, Utah 84108, USA. Oleg.Borodin@utah.edu
A new polarizable force field accurately predicts properties of diverse ionic liquids and other organic molecules. This development enhances molecular dynamics simulations for materials science and chemistry research.
Area of Science:
- Computational Chemistry
- Materials Science
- Physical Chemistry
Background:
- Accurate molecular simulations require reliable force fields.
- Existing force fields may not fully capture the complex interactions in ionic liquids.
- Polarization effects are crucial for understanding ionic liquid behavior.
Purpose of the Study:
- To develop and validate a many-body polarizable force field for a wide range of ionic liquids.
- To assess the force field's accuracy in predicting thermodynamic and transport properties.
- To investigate the impact of polarization on ionic liquid structure and dynamics.
Main Methods:
- Development of a many-body polarizable force field.
- Classical molecular dynamics (MD) simulations of 30 ionic liquids at various temperatures.
- Validation against experimental data for density, heat of vaporization, ion diffusion, conductivity, and viscosity.
- Testing on ionic crystal cell parameters and investigation of polarization effects in [emim][BF(4)].
Main Results:
- The developed force field shows good agreement with experimental data for numerous ionic liquids.
- Accurate prediction of thermodynamic and transport properties for various organic solvents.
- Demonstrated ability to predict ionic crystal cell parameters.
- Revealed a connection between polarization, structural changes, and slowed ion dynamics.
Conclusions:
- The many-body polarizable force field is a robust tool for simulating ionic liquids and related organic compounds.
- Polarization significantly influences the structure and ion transport properties of ionic liquids.
- The validated force field enables more accurate predictions in computational chemistry and materials science.
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