Min-map bias Monte Carlo for chain molecules: biased Monte Carlo sampling based on bijective minimum-to-minimum...

Area of Science:

• Computational Chemistry
• Statistical Mechanics
• Molecular Simulation

Background:

• Monte Carlo (MC) simulations are crucial for molecular modeling.
• Low trial move acceptance rates hinder simulations of explicit molecular systems.
• Existing methods struggle with efficiency in complex molecular descriptions.

Purpose of the Study:

• Introduce a novel Monte Carlo scheme, min-map bias MC.
• Enhance the efficiency of molecular simulations, particularly for systems with low acceptance rates.
• Address the limitations of traditional MC methods in complex scenarios.

Main Methods:

• Developed a min-map bias Monte Carlo scheme based on Theodorou's bijective mapping.
• Integrated the new scheme with existing bare MC methods.
• Applied the method to simulate linear alkanes using continuum configurational bias.

Main Results:

• The min-map bias MC method effectively transfers moves between configuration spaces (Omega(0) to Omega(1)).
• Achieved significantly higher trial move acceptance rates compared to standard MC.
• Successfully alleviated low acceptance rates in explicit molecular simulations of alkanes.

Conclusions:

• Min-map bias Monte Carlo is a powerful technique for improving simulation efficiency.
• The method offers a viable solution for overcoming low acceptance rates in complex molecular systems.
• This advancement facilitates more accurate and feasible molecular simulations.