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Data-driven inverse design and optimisation of silica aerogel model networks

Pandit, Prakul and Abdusalamov, Rasul and Itskov, Mikhail and Milow, Barbara and Rege, Ameya Govind (2023) Data-driven inverse design and optimisation of silica aerogel model networks. Proceedings in Applied Mathematics and Mechanics, 23 (1). Wiley. doi: 10.1002/pamm.202200329. ISSN 1617-7061.

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Abstract

Silica aerogels are highly porous ultralight materials with extremely low density and thermal conductivity. These exceptional properties of silica aerogels are often accounted to microstructure morphology, thus making them of keen research interest for analysing their structure-property relationships. The classical approach for this involved the microstructure modelling of the silica aerogels with aggregation-based modelling algorithm viz., diffusion-limited cluster-cluster aggregation (DLCA) and then performing finite element method (FEM) on the generated representative volume element (RVEs). However, the process often requires large computation time and resources. The objective of this work was thus to introduce an artificial intelligence approach based on neural networks and reinforcement learning to eliminate the necessity of generating and simulating 3D silica aerogel models for predicting their structural and mechanical properties. To this end for the forward prediction of the elastic modulus and fractal dimension of the silica aerogels from DLCA parameters, an artificial neural network was developed. Furthermore, to reverse engineer the material and perform inverse material design, a reinforcement learning framework was developed, that is shown to have learned to determine appropriate DLCA model parameters as actions for a desired fractal dimension and elastic modulus.

Item URL in elib:https://elib.dlr.de/197856/
Document Type:Article
Title:Data-driven inverse design and optimisation of silica aerogel model networks
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Pandit, PrakulUNSPECIFIEDhttps://orcid.org/0000-0002-1343-3046146022945
Abdusalamov, RasulRWTH Aachen UniversityUNSPECIFIEDUNSPECIFIED
Itskov, MikhailRWTH Aachen UniversityUNSPECIFIEDUNSPECIFIED
Milow, BarbaraUNSPECIFIEDhttps://orcid.org/0000-0002-6350-7728UNSPECIFIED
Rege, Ameya GovindUNSPECIFIEDhttps://orcid.org/0000-0001-9564-5482UNSPECIFIED
Date:31 May 2023
Journal or Publication Title:Proceedings in Applied Mathematics and Mechanics
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Volume:23
DOI:10.1002/pamm.202200329
Publisher:Wiley
ISSN:1617-7061
Status:Published
Keywords:machine learning, material informatics, artificial intelligence in materials, silica aerogels
HGF - Research field:other
HGF - Program:other
HGF - Program Themes:other
DLR - Research area:Digitalisation
DLR - Program:D KIZ - Artificial Intelligence
DLR - Research theme (Project):D - ReBAR
Location: Köln-Porz
Institutes and Institutions:Institute of Materials Research > Aerogels and Aerogel Composites
Deposited By: Pandit, Prakul
Deposited On:06 Nov 2023 10:08
Last Modified:22 Nov 2023 13:26

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