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Area of Science:

  • Computational chemistry
  • Materials science
  • Statistical mechanics

Background:

  • Molecular dynamics (MD) simulations are crucial for calculating diffusion coefficients.
  • Finite-size effects in MD simulations introduce inaccuracies.
  • Existing Yeh-Hummer (YH) corrections are established for single-component fluids but less effective for binary mixtures.

Purpose of the Study:

  • To develop improved finite-size correction factors for diffusion coefficients in binary Lennard-Jones (LJ) fluid mixtures.
  • To enhance the accuracy of MD-simulated diffusion rates for both self-diffusion and Maxwell-Stefan (MS) diffusion.
  • To investigate the efficacy of empirical and machine learning methods for correction factor development.

Main Methods:

  • Application of empirical methods and artificial neural networks (ANNs) to analyze finite-size effects.
  • Development and testing of novel correction factors for self-diffusion and MS diffusion in binary LJ mixtures.
  • Comparison of ANN-based corrections against established Yeh-Hummer (YH) corrections.

Main Results:

  • ANN models reduced diffusion errors by an order of magnitude compared to existing YH corrections.
  • The proposed ANN corrections demonstrate high performance even for mixtures with significant size and energy dissimilarities.
  • Existing YH corrections were found insufficient for complex binary mixtures.

Conclusions:

  • Machine learning, specifically ANNs, offers a powerful approach to significantly improve finite-size corrections for MD diffusion simulations.
  • The developed ANN models provide more accurate diffusion coefficients for binary fluid mixtures, especially those with challenging properties.
  • This work advances the reliability of MD simulations for predicting transport properties in complex fluid systems.