Visual Analysis of Charge Flow Networks for Complex Morphologies

Abstract

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.

Visual Analysis of Electronic Densities and Transitions in Molecules

“…Here, the nodes are arranged along a circle and connected by arcs scaled according to the ratio of the out‐ and in‐flow of the respective nodes. Such diagrams have been used in many applications including charge flow networks [KFM ∗ 19]. Considering weighted bipartite graphs as a special case of more general flow graphs or transition matrices, all related graph drawing methods are principally applicable [NMSL19].…”

Inviwo — A Visualization System with Usage Abstraction Levels

“…Inviwo has successfully been used in numerous scientific publications within different application areas, commercial products, and university-level courses. The scientific contributions include work on advanced volumetric illumination [35]- [37], medical and molecular visualization [38]- [40], transfer function design for volume rendering [21], [41], crowd-sourcing-based user studies [42]- [44], topological analysis [45]- [47], as well as multi-variate data and flow visualization [48]- [50]. In addition, Inviwo is to the best of our knowledge currently used by four different universities and two commercial companies.…”