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Thermodynamic extrapolation accurately predicts water

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Area of Science:

  • Computational chemistry
  • Physical chemistry
  • Statistical mechanics

Background:

  • Thermodynamic extrapolation predicts molecular simulation observables at different conditions.
  • This reduces computational cost for phase and structural transitions.

Purpose of the Study:

  • Explore limitations and accuracy of thermodynamic extrapolation for water.
  • Investigate shifts in liquid structure due to temperature and density changes.

Main Methods:

  • Formulas for volume extrapolation in canonical ensembles.
  • Linear extrapolation in temperature and volume.
  • Comparison with classical perturbation theory.
  • Analysis of an ideal gas in an external field.
  • Recursive interpolation strategy.

Main Results:

  • Linear extrapolation in volume is accurate only over a limited density range for water.
  • Linear extrapolation in temperature is accurate across the entire liquid state.
  • Exact relationships between extrapolation and free energy prediction techniques are demonstrated.
  • Recursive interpolation successfully maps qualitative shifts in water structure with density.

Conclusions:

  • Thermodynamic extrapolation offers a computationally efficient method for exploring fluid behavior.
  • Temperature extrapolation is more robust than volume extrapolation for water.
  • Recursive interpolation provides a powerful tool for mapping fluid properties over a range of conditions.