elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Machine learning-based structure–property predictions in silica aerogels

Abdusalamov, Rasul and Pandit, Prakul and Milow, Barbara and Itskov, Mikhail and Rege, Ameya Govind (2021) Machine learning-based structure–property predictions in silica aerogels. Soft Matter, 17 (31), pp. 7350-7358. Royal Society of Chemistry. doi: 10.1039/D1SM00307K. ISSN 1744-683X.

[img] PDF - Preprint version (submitted draft)
5MB

Official URL: https://pubs.rsc.org/en/content/articlelanding/2021/SM/D1SM00307K

Abstract

The structural features in silica aerogels are known to be modelled effectively by the diffusion-limited cluster–cluster aggregation (DLCA) approach. In this paper, an artificial neural network (ANN) is developed for predicting the fractal properties of silica aerogels, given the input parameters for a DLCA algorithm. This approach of machine learning substitutes the necessity of first generating the DLCA structures and then simulating and characterising their fractal properties. The developed ANN demonstrates the capability of predicting the fractal dimension for any given set of DLCA parameters within an accuracy of R2 = 0.973. Furthermore, the same ANN is subsequently inverted for predicting the input parameters for reconstructing a DLCA model network of silica aerogels, for a given desired target fractal dimension. There, it is shown that the fractal dimension is not a unique characteristic defining the network structure of silica aerogels, and the same fractal dimension can be obtained for different sets of DLCA input parameters. However, the problem of non-uniqueness is solved by using a guided gradient descent approach for predictive modelling purposes within certain bounds of the input parameter-space. Model DLCA structures are generated from the constrained and unconstrained inversion, and are compared against several parameters, amongst them, the pore-size distributions. The constrained inversion of the ANN is shown to predict the DLCA model parameters for a desired fractal dimension within an error of 2%.

Item URL in elib:https://elib.dlr.de/143507/
Document Type:Article
Title:Machine learning-based structure–property predictions in silica aerogels
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Abdusalamov, RasulRWTH Aachen UniversityUNSPECIFIED
Pandit, PrakulUNSPECIFIEDUNSPECIFIED
Milow, BarbaraUNSPECIFIEDhttps://orcid.org/0000-0002-6350-7728
Itskov, MikhailRWTH AachenUNSPECIFIED
Rege, Ameya GovindUNSPECIFIEDhttps://orcid.org/0000-0001-9564-5482
Date:2021
Journal or Publication Title:Soft Matter
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:17
DOI:10.1039/D1SM00307K
Page Range:pp. 7350-7358
Publisher:Royal Society of Chemistry
ISSN:1744-683X
Status:Published
Keywords:aerogel, machine learning, diffusion-limited cluster-cluster aggregation
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Transport System
DLR - Research area:Transport
DLR - Program:V VS - Verkehrssystem
DLR - Research theme (Project):V - Energie und Verkehr
Location: Köln-Porz
Institutes and Institutions:Institute of Materials Research > Aerogels and Aerogel Composites
Deposited By: Rege, Dr. Ameya Govind
Deposited On:07 Oct 2021 08:41
Last Modified:07 Oct 2021 08:41

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Website and database design: Copyright © German Aerospace Center (DLR). All rights reserved.