A Visual Analytics Workflow for Detecting Transition Regions in Large Scale Molecular Dynamics Simulations

Molecular Dynamics (MD) simulations are ubiquitous throughout the physical sci-ences; they are critical in understanding how particle structures evolve over time given a particular energy function. A software package called ParSplice introduced a new method to generate these simulations in parallel that has significantly inflated their length. Typically, simulations are short discrete Markov chains, only captur- ing a few microseconds of a particle’s behavior and containing tens of thousands of transitions between states; in contrast, a typical ParSplice simulation can be as long as a few milliseconds, containing tens of millions of transitions. Naturally, sifting through data of this size is impossible by hand, and there are a number of visualiza- tion systems that provide comprehensive and intuitive analyses of particle structures throughout MD simulations. However, no visual analytics systems have been built that can manage the simulations that ParSplice produces. To analyze these large data-sets, I built a visual analytics system that provides multiple coordinated views that simultaneously describe the data temporally, within its structural context, and based on its properties. The system provides fluid and powerful user interactions regardless of the size of the data, allowing the user to drill down into the data-set to get detailed insights, as well as run and save various calculations, most notably the Nudged Elastic Band method. The system also allows the comparison of multiple trajectories, revealing more information about the general behavior of particles at different temperatures, energy states etc.