Spatial averaging for small molecule diffusion in condensed phase environments.

Area of Science:

• Computational Chemistry
• Statistical Mechanics
• Molecular Dynamics

Background:

• Rare-event sampling is crucial for understanding molecular processes.
• Conventional methods like Metropolis Monte Carlo can be inefficient for complex systems.
• Accurate simulation of molecular diffusion and migration is computationally demanding.

Purpose of the Study:

• To introduce and evaluate spatial averaging as a novel approach for rare-event sampling.
• To apply spatial averaging to multidimensional systems in physical chemistry.
• To compare the efficiency of spatial averaging against conventional Metropolis Monte Carlo.

Main Methods:

• Spatial averaging modifies the importance function to improve sampling efficiency.
• The approach was applied to a CO molecule on amorphous ice, H2 probing amorphous ice, and CO migration in myoglobin.
• Simulation parameters, particularly the averaging radius, were analyzed for consistency across systems.

Main Results:

• Spatial averaging demonstrated superior performance compared to conventional Metropolis Monte Carlo across all tested systems.
• Optimal simulation parameters were found to be broadly applicable across different molecular systems.
• Facile migration of H2 in amorphous ice was confirmed, with low free energy barriers (<1 kcal/mol).
• Spatial averaging successfully identified all known metastable states for CO in myoglobin.

Conclusions:

• Spatial averaging significantly enhances the sampling of configurational space in molecular simulations.
• The method offers a more efficient and accurate approach for studying rare events in physical chemistry.
• This technique holds promise for advancing our understanding of molecular dynamics and material properties.