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Deep reinforcement learning for microstructural optimisation of silica aerogels

Pandit, Prakul and Abdusalamov, Rasul and Itskov, Mikhail and Rege, Ameya Govind (2024) Deep reinforcement learning for microstructural optimisation of silica aerogels. Scientific Reports, 14, p. 1511. Nature Publishing Group. doi: 10.1038/s41598-024-51341-y. ISSN 2045-2322.

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Official URL: https://www.nature.com/articles/s41598-024-51341-y


Silica aerogels are being extensively studied for aerospace and transportation applications due to their diverse multifunctional properties. While their microstructural features dictate their thermal, mechanical, and acoustic properties, their accurate characterisation remains challenging due to their nanoporous morphology and the stochastic nature of gelation. In this work, a deep reinforcement learning (DRL) framework is presented to optimise silica aerogel microstructures modelled with the diffusion-limited cluster–cluster aggregation (DLCA) algorithm. For faster computations, two environments consisting of DLCA surrogate models are tested with the DRL framework for inverse microstructure design. The DRL framework is shown to effectively optimise the microstructure morphology, wherein the error of the material properties achieved is dependent upon the complexity of the environment. However, in all cases, with adequate training of the DRL agent, material microstructures with desired properties can be achieved by the framework. Thus, the methodology provides a resource-efficient means to design aerogels, offering computational advantages over experimental iterations or direct numerical solutions.

Item URL in elib:https://elib.dlr.de/202191/
Document Type:Article
Title:Deep reinforcement learning for microstructural optimisation of silica aerogels
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Pandit, PrakulUNSPECIFIEDhttps://orcid.org/0000-0002-1343-3046151555193
Abdusalamov, RasulRWTH Aachen UniversityUNSPECIFIEDUNSPECIFIED
Rege, Ameya GovindUNSPECIFIEDhttps://orcid.org/0000-0001-9564-5482UNSPECIFIED
Date:17 January 2024
Journal or Publication Title:Scientific Reports
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Page Range:p. 1511
Publisher:Nature Publishing Group
Keywords:dlca, drl, machine learning, surrogate, model, silica, aerogel, optimisation
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: Rege, Dr. Ameya Govind
Deposited On:25 Jan 2024 09:37
Last Modified:25 Jan 2024 09:37

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