Minisymposium Presentation

Molecular dynamics (MD) simulations generate extensive data to analyze molecular behaviors, necessitating supercomputers for execution and post-simulation analysis. Traditional approaches store simulation data for later analysis, causing bottlenecks that delay discoveries and limit the analysis of saved data. Performing data processing and analysis as generated eliminates storage overheads and reduces I/O costs and time. This talk examines a software framework for early termination of MD simulations using collective variables (CVs) at runtime. Early termination can save computational resources by redirecting them to unexplored conformational space regions. CVs identify significant conformational states, such as a folded protein structure, and trigger early termination. We compare full simulations of the FS peptide on the Summit supercomputer with early-terminated simulations using CVs like Largest Eigen Value (LEV) and Effective Sample Size (ESS). Results show that LEV and ESS terminated simulations preserve critical conformational states. Root Mean Square Deviation (RMSD) analysis confirms the alignment of conformational spaces, and Hidden Markov Models (HMM) demonstrate maintained dynamic behavior. Our framework's early termination offers a robust representation of conformational space while optimizing computational efficiency.