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Modeling Equilibrium Solid-Liquid Interfaces under Effective Constant Chemical Potential Using Machine Learning

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We developed iterative quasi-constant chemical potential molecular dynamics (iqCμMD) to simulate targeted concentrations in solutions. This method efficiently models interfaces under bulk conditions comparable to experiments, overcoming conventional molecular dynamics limitations.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Physical chemistry

Background:

  • Chemical potential (μ) is crucial for interfacial processes.
  • Conventional molecular dynamics (MD) struggles to maintain constant chemical potential, especially under dynamic conditions.
  • Solute migration to interfaces can alter bulk concentrations, limiting simulation accuracy.

Purpose of the Study:

  • Introduce iterative quasi-constant chemical potential molecular dynamics (iqCμMD) for simulating targeted molar concentrations.
  • Enable MD simulations of interfaces under bulk conditions comparable to experimental settings.
  • Provide a computationally efficient method to overcome limitations of fixed-composition MD.

Main Methods:

  • Developed and implemented the iqCμMD simulation approach.
  • Applied iqCμMD with machine learning interatomic potentials (MLIPs) for Na2SO4(aq)-graphene interface.
  • Validated transferability using classical force fields for NaCl(aq)-air and NaCl(aq)-graphite interfaces.

Main Results:

  • iqCμMD efficiently achieves target bulk ion concentrations within two iterations.
  • MLIP-driven iqCμMD enables converged results with smaller-scale simulations.
  • Results are comparable to previous CμMD simulations and DFT-level accuracy.

Conclusions:

  • iqCμMD offers a robust and simple computational framework for constant chemical potential simulations.
  • The method requires only the ability to converge interfacial simulations with a measurable bulk region.
  • Combines iqCμMD with MLIPs for accurate modeling of solid-liquid interfaces under effective constant chemical potential.