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Self-averaging parameter estimation for coarse-grained particle models
Carlos Monago1, J A de la Torre1, Pep Español1
1Department Física Fundamental, Universidad Nacional de Educación a Distancia, Madrid, Spain.
The Journal of Chemical Physics
|July 22, 2026
Summary
We developed a new method using microscopic data to estimate parameters in coarse-grained models. This approach accurately determines both static and dynamic parameters, improving model predictability.
Area of Science:
- Statistical Mechanics
- Computational Physics
- Chemical Physics
Background:
- Coarse-grained models simplify complex systems but require accurate parameterization.
- Estimating parameters, especially dynamic ones like friction, remains a challenge.
Purpose of the Study:
- To introduce a novel parameter estimation method using microscopic data.
- To extend parameter estimation to both static and dynamic properties in coarse-grained models.
- To validate the method's accuracy and applicability.
Main Methods:
- Coupling stochastic differential equations with dynamic parameter equations.
- Utilizing microscopic averages and correlations of observables.
- Applying Anosov-Kifer's theorem for system self-averaging.
Main Results:
- Successfully estimated static and dynamic parameters for a Brownian particle in a harmonic potential.
- Accurately determined position-dependent hydrodynamic mobility for interacting Brownian particles.
- Inferred potential of mean force and hydrodynamic mobility for a bimodal-mass Lennard-Jones fluid.
Conclusions:
- The developed method provides accurate parameter estimation for coarse-grained models.
- It effectively determines both static and dynamic parameters, including complex transport properties.
- This approach enhances the predictive power of coarse-grained simulations.
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