Issue 29, 2023

nanoNET: machine learning platform for predicting nanoparticles distribution in a polymer matrix

Abstract

Polymer nanocomposites (PNCs) offer a broad range of thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal composition–property relationship in PNCs due to their wide-ranging composition and chemical space. Here, we address this problem and develop a new method to model the composition–microstructure relation of a PNC through an intelligent machine–learning pipeline named nanoNET. The nanoNET is a nanoparticles (NPs) distribution predictor, built upon computer vision and image recognition concepts. It integrates unsupervised deep learning and regression in a fully automated pipeline. We conduct coarse-grained molecular dynamics simulations of PNCs and utilize the data to establish and validate the nanoNET. Within this framework, a random forest regression model predicts the distribution of NPs in a PNC in a latent space. Subsequently, a convolutional neural network-based decoder converts the latent space representation to the actual radial distribution function (RDF) of NPs in the given PNC. The nanoNET predicts NPs distribution in many unknown PNCs very accurately. This method is very generic and can accelerate the design, discovery, and fundamental understanding of composition–microstructure relationships in PNCs and other molecular systems.

Graphical abstract: nanoNET: machine learning platform for predicting nanoparticles distribution in a polymer matrix

Supplementary files

Article information

Article type
Paper
Submitted
01 Mey 2023
Accepted
28 Jun 2023
First published
29 Jun 2023

Soft Matter, 2023,19, 5502-5512

nanoNET: machine learning platform for predicting nanoparticles distribution in a polymer matrix

K. Ayush, A. Seth and T. K. Patra, Soft Matter, 2023, 19, 5502 DOI: 10.1039/D3SM00567D

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements