Coarse-Graining Self-Assembly by the Stochastic Landscape Method.

Area of Science:

• Statistical physics
• Complex systems modeling
• Nanotechnology and material science

Background:

• Inferring dynamics of many-body stochastic systems is challenging, especially for nonequilibrium self-assembly.
• Markov state models (MSMs) are powerful for dynamic behavior but hard to construct for complex systems.
• Existing methods often need extensive data or system-specific assumptions.

Purpose of the Study:

• Introduce a novel framework for constructing MSMs of nonequilibrium self-assembly.
• Utilize the stochastic landscape method (SLM) for a physically grounded approach.
• Enable predictive control and accurate modeling of assembly dynamics.

Main Methods:

• Constructing MSMs based on the stochastic landscape method (SLM).
• Using a tractable amount of simulation data for coarse-graining.
• Developing a low-dimensional model from high-dimensional phase space.

Main Results:

• The method accurately reproduces key dynamic observables like yield and first assembly times.
• The resulting MSM generalizes beyond construction conditions, predicting new parameter regimes.
• Computational cost is reduced by several orders of magnitude compared to baseline simulations.

Conclusions:

• The SLM-based framework provides an interpretable and efficient approach for MSMs in nonequilibrium self-assembly.
• This method offers a general strategy for modeling complex, many-body stochastic systems.
• Accurate predictions and reduced computational cost are key benefits for diverse scientific applications.