Deep Learning Potential of Mean Force between Polymer Grafted Nanoparticles

Abstract

Grafting polymer chains on nanoparticles surfaces is a well-known route to control their self assembly and distribution in a polymer matrix. A wide variety of self assembled structures are achieved by changing the grafting patterns on an individual nanoparticle surface. However, accurate estimation of the effective potential of mean force between a pair of grafted nanoparticles that determines their assembly and distribution in a polymer matrix is an outstanding challenge in nanoscience. Here, we propose a new deep learning method that learns the interaction between a pair of grafted nanoparticles from the molecular dynamics trajectory of a cluster of polymer-grafted nanoparticles. Subsequently, we carry out the deep learning potential of mean force-based molecular simulation that predicts the self-assembly of a large number of polymer grafted nanoparticles into various anisotropic superstructures, including percolating networks and bilayers depending on nanoparticles concentration in 3D. The deep learning potential of mean force-predicted self-assembled superstructures are consistent with the actual superstructures of polymer grafted nanoparticles. This deep learning framework is very generic and can accelerate the characterization and prediction of the self-assembly and phase behaviour of polymer-grafted and unfunctionalized nanoparticles in free space or a polymer matrix.