Deep Learning Potential of Mean Force between Polymer Grafted Nanoparticles
Sachin Gautham, Tarak Patra

TL;DR
This paper introduces a deep learning approach to accurately estimate the potential of mean force between polymer-grafted nanoparticles, enabling prediction of their self-assembly into complex structures.
Contribution
A novel deep learning method that learns nanoparticle interactions from molecular dynamics data and predicts self-assembly behaviors in various structures.
Findings
Deep learning accurately predicts nanoparticle interactions.
Self-assembled structures match molecular dynamics results.
Framework accelerates analysis of nanoparticle phase behavior.
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…
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Taxonomy
TopicsMaterial Dynamics and Properties · Advanced Polymer Synthesis and Characterization · Electrostatics and Colloid Interactions
