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

Reinforcement Learning for Tailored Development of Aerogels

Pandit, Prakul and Rege, Ameya Govind (2023) Reinforcement Learning for Tailored Development of Aerogels. GCMAC Summer School 2023, 2023-09-18 - 2023-09-22, Karlsruhe, Germany.

[img] PDF - Only accessible within DLR


Ever since Kistler developed the first ‘aerogels’, silica aerogels have been the interest of the scientific community due to their exceptional thermal insulation and lightweight characteristics and suitability for diverse applications [1]. Depending on the nature of synthesis and the application, ranging from thermal insulation in high-temperature applications to their application in lithium sulphur batteries, several intrinsic material characteristics may influence the structure-property relationships of the final aerogel product. However, designing aerogels for specific requirements remains a complex task due to the intricate and nanostructured morphology of the material. Given the recent advancements in the areas of materials research and artificial intelligence, deep reinforcement learning (DRL) provides a solution to such optimisation problems for developing aerogels for achieving targeted properties. With the ability to learn and extract complex patterns and relationships, it provides a data-driven approach to understand and optimise these materials. As such, an offline DRL approach in combination with a property predictor (surrogate model) is presented to optimise computationally designed aerogel microstructures for diverse application. The surrogate models act as intelligent digital twins, eliminating the requirement for iterative computational modelling and the subsequent post-processing. These computational microstructures are modelled with aggregation algorithms mimicking the sol-gel chemistry [2] and gaussian random field-based algorithms [3] to optimise the mechanical and the flow properties of the aerogel microstructures. References [1] M. A. Aegerter, N. Leventis and M. M. Koebel, Aerogels handbook, Springer Science & Business Media, 2011 [2] R. Abdusalamov, C. Scherdel, M. Itskov, B. Milow, A.Rege, J. Phys. Chem. B 2021, 125, 1944–1950. [3] C.J. Gommes, A.P. Roberts Phys. Rev. E, 2008, 77, 041403

Item URL in elib:https://elib.dlr.de/197857/
Document Type:Conference or Workshop Item (Poster)
Title:Reinforcement Learning for Tailored Development of Aerogels
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Pandit, PrakulUNSPECIFIEDhttps://orcid.org/0000-0002-1343-3046144803219
Rege, Ameya GovindUNSPECIFIEDhttps://orcid.org/0000-0001-9564-5482UNSPECIFIED
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:reinforcement learning, lithium sulfur batteries, carbon aerogels, artificial learning
Event Title:GCMAC Summer School 2023
Event Location:Karlsruhe, Germany
Event Type:Workshop
Event Start Date:18 September 2023
Event End Date:22 September 2023
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:19 Oct 2023 10:18
Last Modified:24 Apr 2024 20:58

Repository Staff Only: item control page

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