nanoNET: Machine Learning Platform for Predicting Nanoparticles Distribution in a Polymer Matrix

TL;DR

This paper introduces nanoNET, a machine learning platform that predicts nanoparticle distribution in polymer nanocomposites, aiding in understanding and designing these materials efficiently.

Contribution

The paper presents a novel integrated machine learning pipeline combining unsupervised learning and regression to predict nanoparticle distribution in PNCs.

Findings

nanoNET accurately predicts nanoparticle distribution in unknown PNCs.

The method accelerates the design and discovery of polymer nanocomposites.

It provides a universal approach applicable to various molecular systems.

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 relation of PNCs due to their enormous 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 NPs distribution 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 relations of PNCs and other molecular systems.