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Real-Time Change Point Detection in Molecular Dynamics Streaming Data

DescriptionThe uniform sampling of molecular dynamics (MD) simulations may not accurately capture crucial scientific events. Deep learning approaches are being developed to detect these events within streaming data but can take significant resources on large datasets (PB+). To address these limitations, we propose a solution based on streaming manifold learning, specifically the Kernel CUSUM (KCUSUM) algorithm. By leveraging KCUSUM, we can overcome the limitations of uniform sampling in MD simulations, as it compares incoming data with samples from a reference distribution. It utilizes a statistic derived from the Maximum Mean Discrepancy (MMD) non-parametric testing framework. This algorithm has been tested in various use cases, demonstrating its ability to significantly reduce data rates without missing important scientific events.

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