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DTSTAMP:20250822T115808Z
LOCATION:Room 5.2A17
DTSTART;TZID=Europe/Stockholm:20250617T153000
DTEND;TZID=Europe/Stockholm:20250617T160000
UID:submissions.pasc-conference.org_PASC25_sess121_msa151@linklings.com
SUMMARY:Optimizing Molecular Dynamics Simulations with Runtime Early Termi
 nation Using Collective Variables
DESCRIPTION:Michel Cuendet (University of Geneva)\n\nMolecular dynamics (M
 D) simulations generate extensive data to analyze molecular behaviors, nec
 essitating supercomputers for execution and post-simulation analysis. Trad
 itional approaches store simulation data for later analysis, causing bottl
 enecks that delay discoveries and limit the analysis of saved data. Perfor
 ming data processing and analysis as generated eliminates storage overhead
 s and reduces I/O costs and time. This talk examines a software framework 
 for early termination of MD simulations using collective variables (CVs) a
 t runtime. Early termination can save computational resources by redirecti
 ng them to unexplored conformational space regions. CVs identify significa
 nt conformational states, such as a folded protein structure, and trigger 
 early termination. We compare full simulations of the FS peptide on the Su
 mmit supercomputer with early-terminated simulations using CVs like Larges
 t Eigen Value (LEV) and Effective Sample Size (ESS). Results show that LEV
  and ESS terminated simulations preserve critical conformational states. R
 oot Mean Square Deviation (RMSD) analysis confirms the alignment of confor
 mational spaces, and Hidden Markov Models (HMM) demonstrate maintained dyn
 amic behavior. Our framework's early termination offers a robust represent
 ation of conformational space while optimizing computational efficiency.\n
 \nDomain: Chemistry and Materials, Engineering, Physics, Computational Met
 hods and Applied Mathematics\n\nSession Chairs: Ewa Deelman (University of
  Southern California) and Michela Taufer (University of Tennessee)\n\n
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