Enhanced configurational sampling with hybrid non-equilibrium molecular dynamics-Monte Carlo propagator

Area of Science:

• Computational Chemistry
• Molecular Modeling
• Biophysics

Background:

• Molecular dynamics (MD) simulations are crucial for exploring molecular configurations but often suffer from slow convergence on rugged potential energy surfaces.
• Existing methods to smooth potential energy surfaces require complex reweighting or replica-exchange schemes to maintain equilibrium probability distributions.

Purpose of the Study:

• To develop a novel hybrid sampling propagator that combines Metropolis-Hastings Monte Carlo (MC) with non-equilibrium MD (neMD).
• To enhance the efficiency of molecular simulations by accelerating the sampling of configurational space for complex systems.

Main Methods:

• A hybrid neMD-MC propagator was developed, involving equilibrium MD, a 'boosting phase' with a time-dependent Hamiltonian, and Metropolis acceptance criteria.
• The method ensures microscopic detailed balance and accurate Boltzmann distributions using a symmetric momentum reversal prescription.
• The propagator was integrated with accelerated MD, solute tempering, and adaptive biased force sampling.

Main Results:

• Illustrative tests on biomolecular systems demonstrated a significant speedup in simulation convergence.
• The hybrid approach effectively enhances sampling efficiency compared to traditional methods.
• The method rigorously yields the equilibrium Boltzmann distribution.

Conclusions:

• The hybrid neMD-MC sampling propagator offers a powerful and efficient approach for molecular simulations.
• This method overcomes the slow convergence limitations of brute-force MD for complex systems.
• The technique shows promise for accelerating investigations in computational chemistry and biophysics.