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Likelihood-based non-Markovian models from molecular dynamics.

Hadrien Vroylandt1, Ludovic Goudenège2, Pierre Monmarché3,4

  • 1Institut des Sciences du Calcul et des Données, Sorbonne Université, F-75005 Paris, France.

Proceedings of the National Academy of Sciences of the United States of America
|March 23, 2022
PubMed
Summary
This summary is machine-generated.

We developed a new method to create simplified models of complex systems. This approach maximizes data likelihood, enabling efficient study of chemical reactions and biomolecular dynamics.

Keywords:
coarse-grained modelsdata-driven parametrizationgeneralized Langevin equationmaximum likelihood

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Area of Science:

  • Statistical Mechanics
  • Computational Chemistry
  • Biophysics

Background:

  • Complex systems analysis often requires reducing high-dimensional data to low-dimensional collective variables for better physical understanding.
  • Generalized Langevin equations (GLEs) are used to model the dynamics of these reduced variables, but require accurate coefficient estimation from simulations.
  • GLEs incorporate a memory kernel that captures the interactions between the collective variables and their environment.

Purpose of the Study:

  • To introduce and implement a novel approach for deriving generalized Langevin equations.
  • To ensure the derived GLEs efficiently capture the essential dynamics of complex systems.
  • To provide a method for generating accurate reduced models from high-dimensional simulation data.

Main Methods:

  • Developed a maximum likelihood estimation framework for fitting generalized Langevin equation coefficients.
  • Implemented the approach using simulation data from complex systems.
  • Focused on optimizing the memory kernel estimation within the GLE framework.

Main Results:

  • Successfully generated reduced models of complex systems using the maximum likelihood approach.
  • Demonstrated the efficiency of the method in capturing system dynamics.
  • The approach provides a robust way to estimate GLE coefficients, including the memory kernel.

Conclusions:

  • The maximum likelihood approach offers an efficient strategy for creating accurate, low-dimensional models of complex systems.
  • This method facilitates the study of dynamical properties in diverse fields, including chemical reactions, biomolecular conformational changes, and phase transitions.
  • The developed technique enhances the physical understanding and computational tractability of complex system dynamics.