Molecular dynamics (MD) plays a crucial role in research across diverse fields such as biochemistry, materials science, pharmaceutical drug design, and physics. By simulating and analyzing the dynamics of molecular systems, researchers aim to bridge observable and unobservable scales by replicating experimental outcomes through computational methods. These simulations generate molecular trajectories that capture the temporal evolution of molecular systems, providing rich datasets for further study. To analyze this data, researchers rely on a combination of visualization and analysis tools to construct and study properties and structures to gain insights. However, the fragmentation of tools in typical workflows disrupts the exploration process as the tools cannot share contextual information, leading to inefficiencies.

This work introduces methods to enhance the core aspects of molecular analysis: the definition, evaluation, and visualization of molecular properties and structures. By framing MD analysis as a practical application of the visualization pipeline, we establish guiding principles aimed at improving the entire process, shaping the work presented in this thesis.

Visualization of molecular structures, composed of infinitesimal particles, relies on mapping molecular properties into geometric and color representations. Through geometric shape and color, we can emulate light interactions, crucial for perceiving depth and spatial relations in three-dimensional environments. Spatial relations play an essential role in the dynamics of molecular systems, as intermolecular interactions depend on proximity. To encode the spatial relations over time, we employ Spatial Distribution Functions (SDF), a technique which extracts reference frames inherent to structures that are used to record the occurrences of atoms. This work incorporates the SDF as an integral part of the analysis workflow and extends its capabilities to support the aggregation of structural instances.

Beyond the SDF, the work presents a set of visual analysis components tailored for MD analysis. By unifying these components within a single software tool, we streamline the workflow, enabling tighter integration, improved communication between components, and enhanced user interaction. This unified approach reduces the time required for each exploratory cycle and enhances the overall analysis experience.

The culmination of this work is VIAMD, a software for interactive visual analysis of molecular dynamics. A key challenge in developing such interactive tools is ensuring efficient hardware utilization to handle large datasets and minimize response times. Consequently, a dedicated chapter addresses practical implementation aspects for common MD operations, with the goal of ensuring scalability and performance.

In molecular analysis, Spatial Distribution Functions (SDF) are fundamental instruments in answering questions related to spatial occurrences and relations of atomic structures over time. Given a molecular trajectory, SDFs can, for example, reveal the occurrence of water in relation to particular structures and hence provide clues of hydrophobic and hydrophilic regions. For the computation of meaningful distribution functions, the definition of molecular reference structures is essential. Therefore we introduce the concept of an internal frame of reference (IFR) for labeled point sets that represent selected molecular structures, and we propose an algorithm for tracking the IFR over time and space using a variant of Kabschs algorithm. This approach lets us generate a consistent space for the aggregation of the SDF for molecular trajectories and molecular ensembles. We demonstrate the usefulness of the technique by applying it to temporal molecular trajectories as well as ensemble datasets. The examples include different docking scenarios with DNA, insulin, and aspirin.

While volume rendering for scalar fields has been advanced into a powerful visualisation method, similar volumetric representations for tensor fields are still rare. The complexity of the data challenges not only the rendering but also the design of the transfer function. In this paper we propose an interface using glyph widgets to design a transfer function for the rendering of tensor data sets. Thereby the transfer function (TF) controls a volume rendering which represents sought after tensor-features and a texture that conveys directional information. The basis of the design interface is a two-dimensional projection of the attribute space. Characteristic representatives in the form of glyphs support an intuitive navigation through the attribute space. We provide three different options to select the representatives: automatic selection based on attribute space clustering, uniform sampling of the attribute space, or manually selected representatives. In contrast to glyphs placed into the 3D volume, we use glyphs with complex geometry as widgets to control the shape and extent of the representatives. In the final rendering the glyphs with their assigned colors play a similar role as a legend in an atlas like representation. The method provides an overview of the tensor field in the 3D volume at the same time as it allows the user to explore the tensor field in an attribute space. We demonstrate the flexibility of our approach on tensor fields for selected data sets with very different characteristics.

Selecting components and observing changes ofproperties and configurations over time is an important step in the analysis of molecular dynamics (MD) data. In this paper, we present a selection tool combining text-based queries with spatial selection and filtering. Morphological operations facilitate refinement of the selection by growth operators, e.g. across covalent bonds. The combination of different selection paradigms enables flexible and intuitive analysis on different levels of detail and visual depiction of molecular events. Immediate visual feedback during interactions ensures a smooth exploration of the data. We demonstrate the utility of our selection framework by analyzing temporal changes in the secondary structure of poly-alanineand the binding ofaspirin to phospholipase A2.

Today molecular simulations produce complex data sets capturing the interactions of molecules in detail. Due to the complexity of this time-varying data, advanced visualization techniques are required to support its visual analysis. Current molecular visualization techniques utilize ambient occlusion as a global illumination approximation to improve spatial comprehension. Besides these shadow-like effects, interreflections are also known to improve the spatial comprehension of complex geometric structures. Unfortunately, the inherent computational complexity of interreflections would forbid interactive exploration, which is mandatory in many scenarios dealing with static and time-varying data. In this paper, we introduce a novel analytic approach for capturing interreflections of molecular structures in real-time. By exploiting the knowledge of the underlying space filling representations, we are able to reduce the required parameters and can thus apply symbolic regression to obtain an analytic expression for interreflections. We show how to obtain the data required for the symbolic regression analysis, and how to exploit our analytic solution to enhance interactive molecular visualizations.