title: Nanosystem self-assembly pathways discovered via all-atom multiscale analysis.
abstract: We consider the self-assembly of composite structures from a group of nanocomponents, each consisting of particles within an N-atom system. Self-assembly pathways and rates for nanocomposites are derived via a multiscale analysis of the classical Liouville equation. From a reduced statistical framework, rigorous stochastic equations for population levels of beginning, intermediate, and final aggregates are also derived. It is shown that the definition of an assembly type is a self-consistency criterion that must strike a balance between precision and the need for population levels to be slowly varying relative to the time scale of atomic motion. The deductive multiscale approach is complemented by a qualitative notion of multicomponent association and the ensemble of exact atomic-level configurations consistent with them. In processes such as viral self-assembly from proteins and RNA or DNA, there are many possible intermediates, so that it is usually difficult to predict the most efficient assembly pathway. However, in the current study, rates of assembly of each possible intermediate can be predicted. This avoids the need, as in a phenomenological approach, for recalibration with each new application. The method accounts for the feedback across scales in space and time that is fundamental to nanosystem self-assembly. The theory has applications to bionanostructures, geomaterials, engineered composites, and nanocapsule therapeutic delivery systems.
authors.affiliation: Department of Mathematics, United States Naval Academy, Annapolis, Maryland 21402, United States. pankavic@usna.edu
authors.forename: Stephen D
authors.initials: SD
authors.lastname: Pankavich
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Ortoleva
mesh_terms: D003198:Computer Simulation; D019281:Dimerization; D008956:Models, Chemical; D008958:Models, Molecular; D049329:Nanostructures / Q000737:chemistry*; D036103:Nanotechnology / Q000379:methods*; D013269:Stochastic Processes; D013816:Thermodynamics