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Learning Coarse-Grained Potentials for Binary Fluids.

Peiyuan Gao1, Xiu Yang2, Alexandre M Tartakovsky1

  • 1Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.

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|July 16, 2020
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Summary
This summary is machine-generated.

A new probabilistic machine learning model enhances coarse-grained (CG) force fields for predicting liquid-liquid interface properties. This method overcomes limitations in parameter determination, improving interfacial tension and structure predictions for binary fluid mixtures like water-hexane.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Statistical Mechanics

Background:

  • Accurate prediction of interfacial properties in multiple-fluid systems using coarse-grained (CG) models remains a challenge.
  • Existing CG force fields often struggle with curvature-dependent interfacial behavior.
  • Parameterization of CG models requires robust methods to ensure accurate representation of physical systems.

Purpose of the Study:

  • To develop a novel probabilistic machine learning (ML) model for learning CG potentials in binary fluid systems.
  • To address the challenge of non-unique parameter sets in standard response-surface approaches for CG force fields.
  • To improve the prediction accuracy of interfacial structure and thermodynamic properties for liquid-liquid interfaces.

Main Methods:

  • Development of a new CG force field (FF) within the Shinoda-DeVane-Klein (SDK) framework.
  • Implementation of a probabilistic ML approach to compute the probability density function (PDF) of FF parameters.
  • Validation against atomistic simulations and existing CG FFs for the water-hexane mixture.

Main Results:

  • The probabilistic ML method successfully identifies a unique set of parameters for the CG FF, overcoming issues with multiple shallow minima in the loss function.
  • The new CG FF, parameterized using the ML approach, accurately reproduces desired properties of the liquid-liquid interface.
  • The proposed FF demonstrates significantly improved predictions of interfacial tension and structure compared to existing atomistic and CG FFs for the water-hexane system.

Conclusions:

  • The developed probabilistic ML model offers a robust solution for parameterizing CG force fields, particularly for complex interfacial phenomena.
  • This approach enhances the predictive capabilities of CG models for immiscible binary liquid mixtures.
  • The improved CG FF provides a more accurate and efficient tool for simulating and understanding liquid-liquid interfaces in various applications.