MolSieve: A Progressive Visual Analytics System for Molecular Dynamics Simulations

Rostyslav Hnatyshyn, Jieqiong Zhao, Danny Perez, James Ahrens, Ross Maciejewski

Room: 103

2023-10-25T22:24:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
MolSieve is a visual analytics system that reduces large molecular dynamics simulations into their essential components. To accomplish this, GPCCA (Generalized Perron-Cluster-Cluster-Analysis) is applied to a trajectory's transition matrix. The results are then mapped to an easy-to-use and efficient visual representation. MolSieve is scalable and adaptable; multiple large trajectories can be analyzed at once, and many interactions exist to make it easy to compare multiple simulations. MolSieve can be customized through a simple Python programming interface to adapt to any kind of simulation.
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Molecular dynamics, time-series analysis, visual analytics


Molecular Dynamics (MD) simulations are ubiquitous in cutting-edge physio-chemical research. They provide critical insights into how a physical system evolves over time given a model of interatomic interactions. Understanding a system’s evolution is key to selecting the best candidates for new drugs, materials for manufacturing, and countless other practical applications. With today’s technology, these simulations can encompass millions of unit transitions between discrete molecular structures, spanning up to several milliseconds of real time. Attempting to perform a brute-force analysis with data-sets of this size is not only computationally impractical, but would not shed light on the physically-relevant features of the data. Moreover, there is a need to analyze simulation ensembles in order to compare similar processes in differing environments. These problems call for an approach that is analytically transparent, computationally efficient, and flexible enough to handle the variety found in materials-based research. In order to address these problems, we introduce MolSieve, a progressive visual analytics system that enables the comparison of multiple long-duration simulations. Using MolSieve, analysts are able to quickly identify and compare regions of interest within immense simulations through its combination of control charts, data-reduction techniques, and highly informative visual components. A simple programming interface is provided which allows experts to fit MolSieve to their needs. To demonstrate the efficacy of our approach, we present two case studies of MolSieve and report on findings from domain collaborators.