Numerical simulation, clustering, and prediction of multicomponent polymer precipitation
File(s)
Author(s)
Inguva, Pavan
Mason, Lachlan R
Pan, Indranil
Hengardi, Miselle
Matar, Omar K
Type
Journal Article
Abstract
Multicomponent polymer systems are of interest in organic photovoltaic and drug delivery applications, among others where diverse morphologies influence performance. An improved understanding of morphology classification, driven by composition-informed prediction tools, will aid polymer engineering practice. We use a modified Cahn–Hilliard model to simulate polymer precipitation. Such physics-based models require high-performance computations that prevent rapid prototyping and iteration in engineering settings. To reduce the required computational costs, we apply machine learning (ML) techniques for clustering and consequent prediction of the simulated polymer-blend images in conjunction with simulations. Integrating ML and simulations in such a manner reduces the number of simulations needed to map out the morphology of polymer blends as a function of input parameters and also generates a data set which can be used by others to this end. We explore dimensionality reduction, via principal component analysis and autoencoder techniques, and analyze the resulting morphology clusters. Supervised ML using Gaussian process classification was subsequently used to predict morphology clusters according to species molar fraction and interaction parameter inputs. Manual pattern clustering yielded the best results, but ML techniques were able to predict the morphology of polymer blends with ≥90% accuracy.
Date Issued
2020-11-17
Date Acceptance
2020-10-07
Citation
Data-Centric Engineering, 2020, 1
ISSN
2632-6736
Publisher
Cambridge University Press
Journal / Book Title
Data-Centric Engineering
Volume
1
Issue
6
Copyright Statement
© The Author(s), 2020. Published by Cambridge University Press This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Identifier
10.1017/dce.2020.14
Subjects
Classification
image analysis
morphology
polymer blend
prediction
Publication Status
Published
Article Number
e13
Date Publish Online
2020-11-17