Markov State Model Approach to Simulate Self-Assembly

Physical Review X, Journal Year: 2024, Volume and Issue: 14(4)

Published: Dec. 10, 2024

Computational modeling of assembly is challenging for many systems, because their timescales can vastly exceed those accessible to simulations. This article describes the multiMSM, which a general framework that uses Markov state models (MSMs) enable simulating self-assembly and self-organization finite-sized structures on are orders magnitude longer than brute-force dynamics As with traditional MSM approaches, method efficiently overcomes free energy barriers other dynamical bottlenecks. In contrast previous approaches assembly, simultaneous clusters consequent depletion subunits or small oligomers. The algorithm accounts changes in transition rates as concentrations monomers intermediates evolve over course reaction. Using two model we show multiMSM accurately predicts full ensemble required reach equilibrium. Importantly, after constructing one system concentration, yields at be approximately calculated without any further sampling. capability allows additional speedup. addition, enables highly efficient calculation quantities such profiles, nucleation timescales, flux along pathways, entropy production rates. Identifying contributions individual transitions reveals sources kinetic traps. broadly applicable systems equilibrium nonequilibrium trivially parallelizable and, thus, scalable. Published by American Physical Society 2024

Language: Английский

Smooth Control of Active Matter

Physics, Journal Year: 2024, Volume and Issue: 17

Published: Feb. 7, 2024

Smooth Control of Active MatterA theoretical study finds that the most energy-efficient way to control an active-matter system is drive it at finite speed-unlike passive-matter systems.

Language: Английский

Control of friction: Shortcuts and optimization for the rate- and state-variable equation

European Journal of Mechanics - A/Solids, Journal Year: 2024, Volume and Issue: 111, P. 105550 - 105550

Published: Dec. 30, 2024

Language: Английский