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Updated: Aug 26, 2025

Using Polystyrene-block-polyacrylic acid-coated Metal Nanoparticles as Monomers for Their Homo- and Co-polymerization

Using Polystyrene-block-polyacrylic acid-coated Metal Nanoparticles as Monomers for Their Homo- and Co-polymerization

Published on: July 9, 2015

Deep learning potential of mean force between polymer grafted nanoparticles.

Sachin M B Gautham¹, Tarak K Patra¹

¹Department of Chemical Engineering, Center for Atomistic Modeling and Materials Design and Center for Carbon Capture Utilization and Storage, Indian Institute of Technology Madras, Chennai, TN 600036, India. tpatra@iitm.ac.in.

|October 13, 2022

Summary

Deep learning accurately predicts nanoparticle self-assembly by learning their interactions. This framework enables precise control over polymer-grafted nanoparticle superstructures, accelerating materials science discovery.

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Last Updated: Aug 26, 2025

Using Polystyrene-block-polyacrylic acid-coated Metal Nanoparticles as Monomers for Their Homo- and Co-polymerization

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Covalent Attachment of Single Molecules for AFM-based Force Spectroscopy

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Published on: March 16, 2020

Area of Science:

Materials Science
Computational Nanoscience

Background:

Grafting polymer chains onto nanoparticles controls their assembly and distribution in polymer matrices.
Predicting the effective potential of mean force between grafted nanoparticles is crucial but challenging.

Purpose of the Study:

To develop a deep learning framework for estimating the potential of mean force between grafted nanoparticles.
To predict the self-assembly behavior of polymer-grafted nanoparticles using this framework.

Main Methods:

Utilized deep learning to learn nanoparticle interactions from molecular dynamics trajectories.
Employed deep learning-derived potential of mean force for molecular simulations.
Simulated self-assembly of single-chain grafted spherical nanoparticles in 3D.

Main Results:

Accurately predicted anisotropic superstructures like networks and bilayers.
Simulated self-assembled structures matched experimental observations.
Demonstrated the framework's generic nature for complex systems.

Conclusions:

Deep learning provides an effective method for characterizing nanoparticle interactions and predicting self-assembly.
This approach accelerates the understanding of phase behavior in polymer-nanoparticle systems.
The framework is extensible to more complex grafted nanoparticle systems.