Self-Assembly of Nanocomponents into Composite Structures: Derivation and Simulation of Langevin Equations

TL;DR

This paper develops a multiscale modeling approach to predict the self-assembly kinetics of nanocomponents into complex structures like viruses and nanocapsules, integrating atomistic details with stochastic dynamics.

Contribution

It derives a Langevin-type equation for order parameters from the Liouville equation, enabling efficient simulation of self-assembly processes with preserved atomic-scale features.

Findings

Derived a Smoluchowski-type equation for order parameters.

Applied method to viral capsid and ribosome assembly.

Enabled faster, more accurate simulations of self-assembly.

Abstract

The kinetics of the self-assembly of nanocomponents into a virus, nanocapsule, or other composite structure is analyzed via a multiscale approach. The objective is to achieve predictability and to preserve key atomic-scale features that underlie the formation and stability of the composite structures. We start with an all-atom description, the Liouville equation, and the order parameters characterizing nanoscale features of the system. An equation of Smoluchowski type for the stochastic dynamics of the order parameters is derived from the Liouville equation via a multiscale perturbation technique. The self-assembly of composite structures from nanocomponents with internal atomic structure is analyzed and growth rates are derived. Applications include the assembly of a viral capsid from capsomers, a ribosome from its major subunits, and composite materials from fibers and nanoparticles. Our approach overcomes errors in other coarse-graining methods which neglect the influence of the nanoscale configuration on the atomistic fluctuations. We account for the effect of order parameters on the statistics of the atomistic fluctuations which contribute to the entropic and average forces driving order parameter evolution. This approach enables an efficient algorithm for computer simulation of self-assembly, whereas other methods severely limit the timestep due to the separation of diffusional and complexing characteristic times. Given that our approach does not require recalibration with each new application, it provides a way to estimate assembly rates and thereby facilitate the discovery of self-assembly pathways and kinetic dead-end structures.