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Partial Charges Optimized by Genetic Algorithms for Deep Eutectic Solvent Simulations.

Xiang Zhong1, Caroline Velez1, Orlando Acevedo1

  • 1Department of Chemistry, University of Miami Coral Gables, Florida 33146, United States.

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A new genetic algorithm machine learning (GAML) software optimizes force field parameters for deep eutectic solvents (DESs). This tool accurately predicts DES properties like density and viscosity, advancing their use in various chemical applications.

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Area of Science:

  • Computational Chemistry
  • Materials Science

Background:

  • Deep eutectic solvents (DESs) are versatile, eco-friendly solvents gaining traction in synthesis and materials science.
  • Optimizing force field (FF) parameters is crucial for accurate molecular simulations of DESs.

Purpose of the Study:

  • To develop and validate a novel software, genetic algorithm machine learning (GAML), for generating optimized potentials for liquid simulation (OPLS)-based FF parameters.
  • To apply GAML for parameterizing eight unique DESs across various salt:HBD ratios.

Main Methods:

  • GAML utilizes a genetic algorithm (GA) approach to optimize FF parameters.
  • Initial validation involved parameterizing 86 conventional solvents, comparing predicted properties to experimental data.
  • Final parameterization focused on DESs composed of three ammonium-based salts and five HBDs.

Main Results:

  • GAML achieved excellent agreement for conventional solvents with mean absolute deviations (MADs) of 0.021 g/cm³ (density), 0.63 kcal/mol (heat of vaporization), and 0.20 kcal/mol (free energy of hydration).
  • For DESs, GAML-generated FF parameters showed exceptional agreement with experimental densities (MAD: 0.024 g/cm³), surface tensions (MAD: 4.2 mN/m), and viscosities (MAD: 5.3 cP) across temperatures.

Conclusions:

  • GAML is a robust tool for developing accurate FF parameters for DESs.
  • The optimized parameters facilitate reliable molecular simulations, supporting the broader application of DESs in chemistry and materials science.