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Area of Science:

  • Computational Chemistry
  • Statistical Mechanics
  • Biophysics

Background:

  • Molecular dynamics simulations generate nonstationary data, posing challenges for traditional modeling.
  • Nonequilibrium processes exhibit biased energy landscapes influenced by finite sampling and external forces.

Purpose of the Study:

  • To extend the data-driven Langevin equation (dLE) approach for modeling nonequilibrium processes.
  • To develop efficient methods for calculating multidimensional Langevin fields in dLE.
  • To validate the dLE's ability to reproduce complex nonequilibrium phenomena.

Main Methods:

  • Construction of a Markovian Langevin model from nonstationary simulation data.
  • Extension of the data-driven Langevin equation (dLE) approach.
  • Development of efficient algorithms for multidimensional Langevin field calculations.

Main Results:

  • The dLE approach successfully models nonequilibrium processes.
  • Accurate reproduction of enforced sodium chloride dissociation in water.
  • Correct simulation of pressure-jump induced nucleation in hard sphere liquids.
  • Effective modeling of helical peptide conformational dynamics from short trajectories.

Conclusions:

  • The extended dLE approach provides a robust framework for analyzing nonequilibrium phenomena in molecular systems.
  • This method accurately accounts for biased energy landscapes and external driving forces.
  • The dLE is a versatile tool for diverse applications in computational and biophysical studies.