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Characterization of porous membranes using artificial neural networks

Zhao, Yinghan 1; Altschuh, Patrick 1; Santoki, Jay ORCID iD icon 1; Griem, Lars 1; Tosato, Giovanna 1; Selzer, Michael 1; Koeppe, Arnd ORCID iD icon 1; Nestler, Britta 1
1 Institut für Angewandte Materialien – Mikrostruktur-Modellierung und Simulation (IAM-MMS), Karlsruher Institut für Technologie (KIT)

Abstract:

Porous membranes have been utilized intensively in a wide range of fields due to their special characteristics and a rigorous characterization of their microstructures is crucial for understanding their properties and improving the performance for target applications. A promising method for the quantitative analysis of porous structures leverages the physics-based generation of porous structures at the pore scale, which can be validated against real experimental microstructures, followed by building the process–structure–property relationships with data-driven algorithms such as artificial neural networks. In this study, a Variational AutoEncoder (VAE) neural network model is used to characterize the 3D structural information of porous materials and to represent them with low-dimensional latent variables, which further model the structure–property relationship and solve the inverse problem of process–structure linkage combined with the Bayesian optimization method. Our methods provide a quantitative way to learn structural descriptors in an unsupervised manner which can characterize porous microstructures robustly.


Verlagsausgabe §
DOI: 10.5445/IR/1000158236
Veröffentlicht am 27.04.2023
Originalveröffentlichung
DOI: 10.1016/j.actamat.2023.118922
Scopus
Zitationen: 4
Web of Science
Zitationen: 3
Dimensions
Zitationen: 4
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Materialien – Mikrostruktur-Modellierung und Simulation (IAM-MMS)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 01.07.2023
Sprache Englisch
Identifikator ISSN: 1359-6454
KITopen-ID: 1000158236
HGF-Programm 38.04.04 (POF IV, LK 01) Geoenergy
Erschienen in Acta Materialia
Verlag Elsevier
Band 253
Seiten Art.-Nr.: 118922
Vorab online veröffentlicht am 19.04.2023
Nachgewiesen in Web of Science
Dimensions
Scopus
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