Prakul Pandit
Deep reinforcement learning for microstructural optimisation of silica aerogels
Pandit, Prakul; Abdusalamov, Rasul; Itskov, Mikhail; Rege, Ameya
Authors
Rasul Abdusalamov
Mikhail Itskov
Ameya Rege
Abstract
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.
Citation
Pandit, P., Abdusalamov, R., Itskov, M., & Rege, A. (in press). Deep reinforcement learning for microstructural optimisation of silica aerogels. Scientific Reports, 14(1), 1511. https://doi.org/10.1038/s41598-024-51341-y
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 3, 2024 |
Online Publication Date | Jan 17, 2024 |
Deposit Date | Jan 22, 2024 |
Journal | Scientific Reports |
Publisher | Nature Publishing Group |
Peer Reviewed | Peer Reviewed |
Volume | 14 |
Issue | 1 |
Pages | 1511 |
DOI | https://doi.org/10.1038/s41598-024-51341-y |
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