DICE: A Monte Carlo Code for Molecular Simulation Including the Configurational Bias Monte Carlo Method

Area of Science:

• Computational Chemistry
• Physical Chemistry
• Molecular Modeling

Background:

• Solute-solvent systems are critical in chemistry and biology.
• Accurate theoretical studies require advanced simulation methods.
• Existing molecular simulation techniques like molecular dynamics (MD) and Monte Carlo (MC) have limitations for complex systems.

Purpose of the Study:

• To enhance the configurational bias Monte Carlo (CBMC) methodology for simulating flexible molecules.
• To introduce a generalized CBMC approach applicable to molecules with diverse fragments.
• To present the new version of the DICE code, incorporating these CBMC enhancements for molecular simulations.

Main Methods:

• Modification of the CBMC acceptance criterion for simplified implementation.
• Generalization of the CBMC method to accommodate various molecular fragments.
• Development and application of the DICE code for molecular simulations, including solute-solvent systems and interfaces.

Main Results:

• Validated the enhanced CBMC implementation using simulations of n-octane and 1,2-dichloroethane.
• Demonstrated efficient conformational sampling for alkanes (n-octane, neopentane, 4-ethylheptane).
• Successfully simulated a complex molecule, boron subphthalocyanine, in vacuum and aqueous solution.

Conclusions:

• The enhanced CBMC method is effective for conformational sampling of complex molecules up to 150 atoms in solution.
• The DICE code with CBMC is a versatile tool for studying solute-solvent systems.
• This work advances theoretical studies of molecular systems by providing a more robust simulation approach.