Systematic bottom-up molecular coarse-graining via force and torque matching using anisotropic particles

Area of Science:

• Computational Chemistry
• Molecular Dynamics Simulations
• Statistical Mechanics

Background:

• Developing accurate coarse-grained (CG) molecular models from fine-grained (all-atom) models is crucial for simulating large condensed-phase systems.
• Existing methods often struggle to capture the complexities of anisotropic particles and finite-temperature effects.

Purpose of the Study:

• To derive a general and systematic method for parameterizing CG molecular models of anisotropic particles.
• To ensure consistency between CG and fine-grained phase-space distributions for accurate force and property derivation.

Main Methods:

• Developed the anisotropic force-matching coarse-graining (AFM-CG) method based on statistical mechanical principles.
• Derived equations for CG forces, torques, masses, and moments of inertia from fine-grained system properties.
• Applied AFM-CG to liquid-state systems of benzene and perylene.

Main Results:

• AFM-CG successfully parameterized CG models for anisotropic organic molecules.
• The resulting CG models demonstrated improved accuracy in describing system properties compared to previous methods.
• The method effectively accounts for finite-temperature and many-body effects on interactions.

Conclusions:

• The AFM-CG method provides a rigorous and accurate approach for developing CG models of anisotropic particles.
• This method is suitable for simulating complex condensed-phase systems like liquid crystals, organic semiconductors, and nucleic acids.
• AFM-CG enables the development of efficient and reliable dynamical simulation models.