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Visual Analysis of Charge Flow Networks for Complex Morphologies
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. SeRC, Sweden.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. SeRC, Sweden.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. SeRC, Sweden.
Linköping University, Department of Science and Technology, Laboratory of Organic Electronics. Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. SeRC, Sweden.
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2019 (English)In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 38, no 3, p. 479-489Article in journal (Refereed) Published
Abstract [en]

In the field of organic electronics, understanding complex material morphologies and their role in efficient charge transport in solar cells is extremely important. Related processes are studied using the Ising model and Kinetic Monte Carlo simulations resulting in large ensembles of stochastic trajectories. Naive visualization of these trajectories, individually or as a whole, does not lead to new knowledge discovery through exploration. In this paper, we present novel visualization and exploration methods to analyze this complex dynamic data, which provide succinct and meaningful abstractions leading to scientific insights. We propose a morphology abstraction yielding a network composed of material pockets and the interfaces, which serves as backbone for the visualization of the charge diffusion. The trajectory network is created using a novel way of implicitly attracting the trajectories to the skeleton of the morphology relying on a relaxation process. Each individual trajectory is then represented as a connected sequence of nodes in the skeleton. The final network summarizes all of these sequences in a single aggregated network. We apply our method to three different morphologies and demonstrate its suitability for exploring this kind of data.

Place, publisher, year, edition, pages
WILEY , 2019. Vol. 38, no 3, p. 479-489
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:liu:diva-160177DOI: 10.1111/cgf.13704ISI: 000481468200038OAI: oai:DiVA.org:liu-160177DiVA, id: diva2:1349564
Note

Funding Agencies|Excellence Center at Linkoping and Lund in Information Technology (ELLIIT); Swedish e-Science Research Centre (SeRC)

Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-10-16

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Kottravel, SathishFalk, MartinMasood, Talha BinLinares, MathieuHotz, Ingrid
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