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

ABSTRACT: Inferring the dynamics of many-body stochastic systems from data remains a fundamental challenge in statistical physics, particularly when models must provide both predictive power and physical interpretability. Nonequilibrium self-assembly, where molecular components are driven to form ordered structures from disordered building blocks, is a prime example, central to nanotechnology, material science, and biology. Markov state models (MSMs) have emerged as a powerful framework for representing and understanding the dynamic behavior of such complex systems by discretizing their high-dimensional phase space into a network of metastable states and transition probabilities. Yet, constructing accurate and interpretable MSMs for nonequilibrium self-assembly remains challenging, often requiring e

SUBMITTER: Faran M 

PROVIDER: S-EPMC12613318 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature