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Bayesian uncertainty quantification and propagation in molecular dynamics simulations: a high performance computing

Panagiotis Angelikopoulos1, Costas Papadimitriou, Petros Koumoutsakos

  • 1Computational Science and Engineering Laboratory, ETH Zürich, CH-8092 Zurich, Switzerland. panagiotis.angelikopoulos@mavt.ethz.ch

The Journal of Chemical Physics
|October 16, 2012
PubMed
Summary

This study introduces a Bayesian framework to accurately estimate uncertainties in molecular dynamics (MD) force field parameters. The method uses parallel computing and adaptive models for efficient simulations of argon.

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Area of Science:

  • Computational chemistry
  • Statistical mechanics
  • Bayesian inference

Background:

  • Molecular dynamics (MD) simulations are crucial for understanding molecular behavior.
  • Accurate force field parameters are essential for reliable MD simulations.
  • Quantifying parameter uncertainties is critical for robust predictions.

Purpose of the Study:

  • To develop a Bayesian probabilistic framework for quantifying and propagating uncertainties in MD force field parameters.
  • To implement a parallel transitional Markov chain Monte Carlo (TMCMC) method for efficient posterior distribution sampling.
  • To reduce computational costs using adaptive surrogate models.

Main Methods:

  • Bayesian probabilistic framework for uncertainty quantification.
  • Highly parallel implementation of transitional Markov chain Monte Carlo (TMCMC).
  • Development of efficient scheduling algorithms for heterogeneous computing clusters.
  • Application of adaptive surrogate models to minimize computational expense.

Main Results:

  • Demonstrated effectiveness of the Bayesian framework in quantifying force field parameter uncertainties.
  • Achieved computational efficiency through parallelization and adaptive modeling.
  • Successfully applied the framework to simulations of liquid and gaseous argon.

Conclusions:

  • The proposed Bayesian framework provides a robust and efficient method for uncertainty quantification in MD force fields.
  • The parallel TMCMC implementation and adaptive surrogate models significantly reduce computational burden.
  • This approach enhances the reliability of MD simulations for materials like argon.