Nonequilibrium Acceleration and Time Forecasting of Cluster-Mediated Self-Assembly
Roy Furman, Michael Faran, Gili Bisker
TL;DR
Nonequilibrium driving speeds up self-assembly processes and can be predicted using specific methods depending on the type of interactions involved.
Contribution
The study systematically evaluates nonequilibrium driving's effectiveness and predictability in self-assembly simulations using different models.
Findings
Nonequilibrium driving significantly reduces the time to first assembly across multiple models.
Predictability of assembly time depends strongly on the nature of particle interactions.
Directed interactions in VMMC systems enhance predictability compared to undirected dynamics.
Abstract
Nonequilibrium driving accelerates self-assembly by breaking the trade-off between thermodynamic stability and kinetic accessibility. While this principle has inspired a variety of theoretical and computational approaches, its effectiveness and predictability within physically realistic simulation frameworks remain to be systematically explored. Here, we investigate its impact using the Virtual-Move Monte Carlo (VMMC) method, a widely adopted approach for simulating collective particle dynamics during self-assembly. We investigate when such acceleration is both effective and predictable for three models, namely, VMMC with directed specific interactions, VMMC with undirected specific interactions, and an undirected single-particle Monte Carlo (SPMC), serving as a benchmark. Across all cases, nonequilibrium driving significantly reduces the time to first assembly, underscoring its…
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Taxonomy
TopicsMicro and Nano Robotics · Modular Robots and Swarm Intelligence · Pickering emulsions and particle stabilization