ExaViz: a Flexible Framework to Analyse, Steer and Interact with Molecular Dynamics Simulations

International audienceThe amount of data generated by molecular dynamics simulations of large molecular assemblies and the sheer size and complexity of the systems studied call for new ways to analyse, steer and interact with such calculations. Traditionally, the analysis is performed off-line once the huge amount of simulation results have been saved to disks, thereby stressing the supercomputer I/O systems, and making it increasingly difficult to handle post-processing and analysis from the scientist's office. The ExaViz framework is an alternative approach developed to couple the simulation with analysis tools to process the data as close as possible to their source of creation, saving a reduced, more manageable and pre-processed data set to disk. ExaViz supports a large variety of analysis and steering scenarios. Our framework can be used for live sessions (simulations short enough to be fully followed by the user) as well as batch sessions (long time batch executions). During interactive sessions, at run time, the user can display plots from analysis, visualise the molecular system and steer the simulation with a haptic device. We also emphasise how a Cave-like immersive environment could be used to leverage such simulations, offering a large display surface to view and intuitively navigate the molecular system

Atomic detail visualization of photosynthetic membranes with GPU-accelerated ray tracing

The cellular process responsible for providing energy for most life on Earth, namely, photosynthetic light-harvesting, requires the cooperation of hundreds of proteins across an organelle, involving length and time scales spanning several orders of magnitude over quantum and classical regimes. Simulation and visualization of this fundamental energy conversion process pose many unique methodological and computational challenges. We present, in two accompanying movies, light-harvesting in the photosynthetic apparatus found in purple bacteria, the so-called chromatophore. The movies are the culmination of three decades of modeling efforts, featuring the collaboration of theoretical, experimental, and computational scientists. We describe the techniques that were used to build, simulate, analyze, and visualize the structures shown in the movies, and we highlight cases where scientific needs spurred the development of new parallel algorithms that efficiently harness GPU accelerators and petascale computers

Advances in Human-Protein Interaction - Interactive and Immersive Molecular Simulations

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Interactive molecular dynamics in virtual reality from quantum chemistry to drug binding: An open-source multi-person framework

© 2019 Author(s). As molecular scientists have made progress in their ability to engineer nanoscale molecular structure, we face new challenges in our ability to engineer molecular dynamics (MD) and flexibility. Dynamics at the molecular scale differs from the familiar mechanics of everyday objects because it involves a complicated, highly correlated, and three-dimensional many-body dynamical choreography which is often nonintuitive even for highly trained researchers. We recently described how interactive molecular dynamics in virtual reality (iMD-VR) can help to meet this challenge, enabling researchers to manipulate real-time MD simulations of flexible structures in 3D. In this article, we outline various efforts to extend immersive technologies to the molecular sciences, and we introduce "Narupa," a flexible, open-source, multiperson iMD-VR software framework which enables groups of researchers to simultaneously cohabit real-time simulation environments to interactively visualize and manipulate the dynamics of molecular structures with atomic-level precision. We outline several application domains where iMD-VR is facilitating research, communication, and creative approaches within the molecular sciences, including training machines to learn potential energy functions, biomolecular conformational sampling, protein-ligand binding, reaction discovery using "on-the-fly" quantum chemistry, and transport dynamics in materials. We touch on iMD-VR's various cognitive and perceptual affordances and outline how these provide research insight for molecular systems. By synergistically combining human spatial reasoning and design insight with computational automation, technologies such as iMD-VR have the potential to improve our ability to understand, engineer, and communicate microscopic dynamical behavior, offering the potential to usher in a new paradigm for engineering molecules and nano-architectures

Visualizing Biological Membrane Organization and Dynamics

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Using Novel Approaches for Navigating Complex Energy Landscapes: Ion Channel Conductance using Hyperdynamics and Human-Guided Global Optimization of Lennard-Jones Clusters

Molecular dynamics (MD) is a widely used tool to study molecular systems on atomic level. However, the timescale of a traditional MD simulation is typically limited to nanoseconds. Thus many interesting processes that occur on microseconds or larger timescale can\u27t be studied. Hyperdynamics provides a way to extend the timescale of MD simulation. In hyperdynamics, MD is performed on a biased potential then corrected to get true dynamics provided certain conditions are met. Here, we tried to study potassium channel conductance using the hyperdynamics method with a bias potential constructed based on the potential of mean force of ion translocation through the selective filter of a potassium ion channel. However, when MD was performed on this biased potential, no ion translocation events were observed. Although some new insights were gained into the rate-limiting steps for ion mobility in this system from these negative results, no further studies are planned with this project. The second project is based on the assumption that hybrid human{computational algorithm is more efficient than purely computational algorithm itself. Such ideas have already been studied by many \crowd-sourcing games, such as Foldit [1] for the protein structure prediction problem, and QuantumMoves [2] for quantum physics. Here, the same idea is applied to cluster structure optimization. A virtual reality android cellphone app was developed to study global optimization of Lennard-Jones clusters with both computational algorithm and hybrid human{computational algorithm. Using linear mixed model analysis, we found statistically significant differences between the expected runtime of both methods, at least for cluster of certain sizes. Further analysis of the data showing human intelligence weakened the strong dependence of the efficiency of the computational method on cluster sizes. We hypothesis that this is due to that humans are able to make large moves that allows the algorithm to cover a large region in the potential energy surface faster. Further studies with more cluster sizes are needed to draw a more complete conclusion. Human intelligence can potentially be integrated into more advanced optimization technique and applied to more complicated optimization problems in the future. Patterns analysis of human behaviors during the optimization process can be conducted to gain insights of mechanisms and strategies of optimization process

Visualization of Time-Varying Data from Atomistic Simulations and Computational Fluid Dynamics

Time-varying data from simulations of dynamical systems are rich in spatio-temporal information. A key challenge is how to analyze such data for extracting useful information from the data and displaying spatially evolving features in the space-time domain of interest. We develop/implement multiple approaches toward visualization-based analysis of time-varying data obtained from two common types of dynamical simulations: molecular dynamics (MD) and computational fluid dynamics (CFD). We also make application case studies. Parallel first-principles molecular dynamics simulations produce massive amounts of time-varying three-dimensional scattered data representing atomic (molecular) configurations for material system being simulated. Rendering the atomic position-time series along with the extracted additional information helps us understand the microscopic processes in complex material system at atomic length and time scales. Radial distribution functions, coordination environments, and clusters are computed and rendered for visualizing structural behavior of the simulated material systems. Atom (particle) trajectories and displacement data are extracted and rendered for visualizing dynamical behavior of the system. While improving our atomistic visualization system to make it versatile, stable and scalable, we focus mainly on atomic trajectories. Trajectory rendering can represent complete simulation information in a single display; however, trajectories get crowded and the associated clutter/occlusion problem becomes serious for even moderate data size. We present and assess various approaches for clutter reduction including constrained rendering, basic and adaptive position merging, and information encoding. Data model with HDF5 and partial I/O, and GLSL shading are adopted to enhance the rendering speed and quality of the trajectories. For applications, a detailed visualization-based analysis is carried out for simulated silicate melts such as model basalt systems. On the other hand, CFD produces temporally and spatially resolved numerical data for fluid systems consisting of a million to tens of millions of cells (mesh points). We implement time surfaces (in particular, evolving surfaces of spheres) for visualizing the vector (flow) field to study the simulated mixing of fluids in the stirred tank