Extrapolation and interpolation strategies for efficiently estimating structural observables as a function of...

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.