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Updated: Jul 15, 2025

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
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Data-Driven Approach to Coarse-Graining Simple Liquids in Confinement.

Ishan Nadkarni1, Haiyi Wu2, Narayana R Aluru1,2

  • 1Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States.

Journal of Chemical Theory and Computation
|October 4, 2023
PubMed
Summary
This summary is machine-generated.

We developed a data-driven framework using Deep Neural Networks (DNN) to determine coarse-grained (CG) Lennard-Jones (LJ) potential parameters for liquids in confined spaces. This method accurately predicts fluid behavior and enhances coarse-graining techniques.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Statistical mechanics

Background:

  • Coarse-graining (CG) methods simplify complex molecular systems for large-scale simulations.
  • Accurate CG potentials are crucial for modeling fluids in confined environments, such as nanoporous materials.
  • Traditional methods for deriving CG parameters can be computationally intensive and may struggle with system-specific effects.

Purpose of the Study:

  • To develop a data-driven framework for identifying coarse-grained Lennard-Jones (LJ) potential parameters in confined systems.
  • To leverage Deep Neural Networks (DNN) to solve the Inverse Liquid State problem (ILST) for confined fluids.
  • To enhance the accuracy and efficiency of coarse-graining simulations for liquids in confined geometries.

Main Methods:

  • A Deep Neural Network (DNN) was trained to approximate the Inverse Liquid State (ILST) solution for confined systems.
  • Transfer learning was employed to predict single-site LJ potentials for multiatomic liquids in slit-like channels.
  • The data-driven approach was combined with the Bottom-Up coarse-graining method using Relative-Entropy (RE) Minimization.

Main Results:

  • The DNN model accurately predicted inhomogeneity effects in confined fluids.
  • Predicted LJ potentials replicated the fluid structure and molecular forces of All-Atom (AA) systems for non-electrostatic interactions.
  • Synergy between the DNN approach and RE minimization significantly improved the robustness and convergence of the iterative RE method.

Conclusions:

  • The proposed data-driven framework effectively identifies CG-LJ parameters for confined liquids.
  • The integration of DNNs with established coarse-graining techniques offers a powerful approach for molecular simulations.
  • This method provides a robust and efficient way to model complex fluid behavior in nanoscale confinement.