Optimizing molecular potential models by imposing kinetic constraints with path reweighting.

Area of Science:

• Computational Chemistry
• Molecular Dynamics Simulations
• Statistical Mechanics

Background:

• Empirical force fields are crucial for molecular dynamics (MD) simulations but are typically optimized for structural and thermodynamic properties.
• Experimental data on interconversion rates between metastable states are rarely incorporated into force fields due to a lack of efficient methods.
• Accurate force fields are essential for predicting the kinetics of molecular processes.

Purpose of the Study:

• To develop a novel framework for optimizing molecular model parameters in force fields using experimental rate constants.
• To enable the incorporation of dynamical information (rate constants) into the force field optimization process.
• To improve the accuracy of molecular dynamics simulations for systems exhibiting rare event dynamics.

Main Methods:

• The approach leverages the statistical mechanics of trajectories to link dynamical observables (rate constants) with molecular model parameters.
• It combines the continuum path ensemble maximum caliber (CPEMC) approach with path reweighting methods for stochastic dynamics.
• The method optimally adapts force field parameters to match predicted and experimental rate constants, selecting solutions that minimize perturbation to the path ensemble.

Main Results:

• The framework successfully optimizes force field parameters by incorporating experimental rate constants.
• Demonstrated validity on test systems, including 2D potentials, molecular isomerization, and protein-ligand unbinding.
• Identified sensitive model components influencing system kinetics, providing physical insights beyond parameter optimization.

Conclusions:

• The developed methodology provides an efficient approach to integrate experimental kinetic data into force field optimization.
• This framework enhances the predictive power of molecular dynamics simulations for complex systems and rare events.
• The approach offers broad implications for refining molecular models and understanding kinetic pathways in various scientific domains.