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

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Nonequilibrium driving speeds up self-assembly by improving efficiency and predictability. Directed interactions in simulations enhance predictive accuracy, offering a robust strategy for complex assembly processes.

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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.