Nonequilibrium Acceleration and Time Forecasting of Cluster-Mediated Self-Assembly

Area of Science:

• Physical Chemistry
• Computational Science
• Materials Science

Background:

• Nonequilibrium driving can overcome limitations in self-assembly by balancing stability and accessibility.
• Previous studies explored this principle theoretically, but systematic evaluation in realistic simulations is needed.

Purpose of the Study:

• To investigate the effectiveness and predictability of nonequilibrium driving in self-assembly simulations.
• To compare different simulation methods and interaction types for self-assembly efficiency.

Main Methods:

• Utilized Virtual-Move Monte Carlo (VMMC) with directed and undirected specific interactions.
• Employed an undirected single-particle Monte Carlo (SPMC) as a benchmark.
• Assessed the Stochastic Landscape Method (SLM) for forecasting assembly dynamics.

Main Results:

• Nonequilibrium driving consistently reduced the time to first assembly across all tested models.
• Predictive power of SLM varied, with directed interactions in VMMC showing higher predictability than undirected dynamics or SPMC.
• Analysis of energy trajectories elucidated the physical basis for differences in predictability.

Conclusions:

• Nonequilibrium driving is a robust strategy for enhancing self-assembly efficiency.
• Directed binding interactions are crucial for improving the predictability of self-assembly processes.
• Understanding interaction dynamics is key to optimizing predictive tools like SLM for complex self-assembly.