Spatial Averaging: Sampling Enhancement for Exploring Configurational Space of Atomic Clusters and Biomolecules.

Area of Science:

• Computational chemistry
• Molecular dynamics
• Statistical mechanics

Background:

• Rare-event problems pose significant challenges for standard simulation methods.
• Spatial averaging Monte Carlo (SA-MC) offers an efficient approach to rare-event studies.
• Existing SA-MC methods require robust implementations for complex systems.

Purpose of the Study:

• To introduce a general and robust implementation of SA-MC within the CHARMM molecular modeling software.
• To develop a procedure for estimating unbiased thermodynamic properties using SA-MC.
• To validate the enhanced SA-MC approach on diverse systems.

Main Methods:

• Developed a SA-MC algorithm incorporating rotational and torsional moves.
• Integrated the SA-MC implementation into the CHARMM software package.
• Proposed a method for unbiased property estimation and applied it to Lennard-Jones clusters and (Ala)2.

Main Results:

• The SA-MC implementation demonstrated superior performance in sampling configurational space compared to standard Metropolis simulations.
• The method achieved faster sampling at minimal additional computational cost.
• Results for (Ala)2 in explicit solvent showed good agreement with prior umbrella sampling simulations.

Conclusions:

• The generalized SA-MC algorithm provides an efficient and robust tool for studying rare-event phenomena in molecular systems.
• The implementation facilitates the computation of unbiased thermodynamic observables.
• SA-MC represents a significant advancement for molecular simulations of complex systems.