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Parametric sensitivity analysis for stochastic molecular systems using information theoretic metrics.

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This study introduces a new sensitivity analysis (SA) method for molecular dynamics simulations. The relative entropy rate (RER) effectively quantifies parameter sensitivities, correlating well with traditional observable functions.

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Area of Science:

  • Computational Chemistry
  • Statistical Mechanics
  • Molecular Dynamics

Background:

  • Stochastic differential equations model continuous time and space Markov processes.
  • Parametric sensitivity analysis is crucial for understanding molecular dynamics models.
  • Existing methods may lack computational tractability for complex systems.

Purpose of the Study:

  • To present an extended parametric sensitivity analysis (SA) methodology for stochastic molecular dynamics.
  • To utilize relative entropy rate (RER) and Fisher information matrix (FIM) for pathwise SA.
  • To enable efficient computation of parameter sensitivities from molecular dynamics simulations.

Main Methods:

  • Developed a pathwise SA method based on RER and FIM for stochastic differential equations.
  • Applied the method to Langevin equation-based molecular dynamics.
  • Computed RER and FIM from averages of the force field using ergodic averages.

Main Results:

  • The RER-based SA method is tractable and computable from single simulation runs.
  • Demonstrated the method's performance on Lennard-Jones fluid and methane liquid systems.
  • Found high correlation between RER-based sensitivities and sensitivities derived from observable functions (RDF, MSD, pressure).

Conclusions:

  • The extended RER-based SA method provides a robust and efficient approach for parameter sensitivity assessment in molecular dynamics.
  • This method offers a valuable alternative for analyzing complex molecular systems.
  • Results validate the utility of information-theoretic quantities for SA in computational chemistry.