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Data driven, predictive molecular dynamics for nanoscale flow simulations under uncertainty.

Panagiotis Angelikopoulos1, Costas Papadimitriou, Petros Koumoutsakos

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Molecular dynamics (MD) simulations and experiments can be reconciled using uncertainty quantification. Integrating more experimental data significantly reduces uncertainties in MD predictions for nanoscale fluid mechanics.

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Area of Science:

  • Computational physics and chemistry
  • Nanotechnology and materials science
  • Statistical mechanics

Background:

  • Molecular dynamics (MD) simulations are crucial for understanding physiological and technological systems, complementing experimental data.
  • Discrepancies between experimental and MD simulation results, particularly in nanofluidics, arise from scale limitations and empirical parameterization.
  • The predictive accuracy of MD simulations is often hampered by uncertainties in parameter calibration and computational implementation.

Purpose of the Study:

  • To consolidate experimental and MD simulation findings through a rigorous uncertainty quantification framework.
  • To assess uncertainties in MD predictions for wetting behavior and hydrophobicity in graphitic nanostructures.
  • To demonstrate how integrating additional experimental data enhances MD model accuracy and reduces predictive uncertainty.

Main Methods:

  • Employed a Bayesian probabilistic framework for large-scale MD simulations.
  • Investigated representative systems: water wetting of graphene, fullerene aggregation, and water transport across carbon nanotubes.
  • Quantified uncertainties in MD predictions for wetting and hydrophobicity.

Main Results:

  • Single-point calibration of MD potentials, like water contact angle on graphene, leads to substantial uncertainties and unreliable predictions.
  • Utilizing additional experimental data demonstrably reduces uncertainty in MD models.
  • The proposed framework successfully consolidates experimental and simulation results, improving quantitative predictions.

Conclusions:

  • Rigorous uncertainty quantification is essential for reconciling MD simulations with experimental data in nanoscale fluid mechanics.
  • Bayesian frameworks and the integration of diverse experimental data enhance the reliability and predictive power of MD simulations.
  • This approach provides a pathway to more accurate and trustworthy computational investigations of complex nanoscale phenomena.