Berlin 2024 – wissenschaftliches Programm
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AKPIK: Arbeitskreis Physik, moderne Informationstechnologie und Künstliche Intelligenz
AKPIK 3: Poster
AKPIK 3.1: Poster
Donnerstag, 21. März 2024, 11:00–14:30, Poster B
A surrogate model for graphene-based conductor materials and the creation of an ontology-based digital twin — •Fabian Teichert1,2,3, Philipp Schulze4,5, Florian Fuchs1,2,3, Martin Stoll4, and Jörg Schuster1,2,3 — 1Fraunhofer Institute for Electronic Nano Systems (ENAS), Chemnitz, Germany — 2Center for Microtechnologies, Chemnitz University of Technology, Chemnitz, Germany — 3Center for Materials, Architectures and Integration of Nanomembranes (MAIN), Chemnitz University of Technology, Chemnitz, Germany — 4Faculty of Mathematics, Chemnitz University of Technology, Chemnitz, Germany — 5Institute of Mathematics, Technische Universität Berlin, Berlin, Germany
(1) We developed a surrogate model for graphene-based conductor materials using Gaussian process regression.
The material of interest is a stack of layers of graphene flakes.
The results of previously performed nodal analysis have been used to obtain a model for calculating in-plane and out-of-plane conductivities much faster.
We present results depending on various material parameters like flake size, packing density, flake conductivity, and inter-layer conductivity.
(2) Recently the Plattform MaterialDigital (www.materialdigital.de) started to facilitate digital twins in material science, i.e. the creation of an ontology-based data storage for the digital representation of (new) materials and their properties in relation to key processing steps.
We participate in this and present an ontology specialized to graphene-based conductor materials, which we fill with simulation data as well as laboratory data.
Keywords: gaussian process regression; surrogate model; platform material digital; digital twin; ontology