Highly parallelizable path sampling with minimal rejections using asynchronous replica exchange and infinite swaps

  • Daniel T. Zhang
  • , Lukas Baldauf
  • , Sander Roet
  • , Anders Lervik
  • , Titus S. van Erp*
  • *Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Capturing rare yet pivotal events poses a significant challenge for molecular simulations. Path sampling provides a unique approach to tackle this issue without altering the potential energy landscape or dynamics, enabling recovery of both thermodynamic and kinetic information. However, despite its exponential acceleration compared to standard molecular dynamics, generating numerous trajectories can still require a long time. By harnessing our recent algorithmic innovations—particularly subtrajectory moves with high acceptance, coupled with asynchronous replica exchange featuring infinite swaps—we establish a highly parallelizable and rapidly converging path sampling protocol, compatible with diverse high-performance computing architectures. We demonstrate our approach on the liquid–vapor phase transition in superheated water, the unfolding of the chignolin protein, and water dissociation. The latter, performed at the ab initio level, achieves comparable statistical accuracy within days, in contrast to a previous study requiring over a year.
Original languageEnglish
Article numbere2318731121
Number of pages9
JournalProceedings of the National Academy of Sciences of the United States of America
Volume121
Issue number7
DOIs
Publication statusPublished - 13 Feb 2024

Bibliographical note

Publisher Copyright:
© 2024 the Author(s). Published by PNAS.

Funding

We thank Zhiliang Zhang for the helpful comments on improving our paper. We acknowledge funding support from the Research Council of Norway (Grant No. 275506). We also thank Sigma2, the National Infrastructure for high-performance computing (HPC) and Data Storage in Norway and the HPC infrastructure IDUN at the NTNU. ACKNOWLEDGMENTS. We thank Zhiliang Zhang for the helpful comments on improving our paper. We acknowledge funding support from the Research Council of Norway (Grant No. 275506). We also thank Sigma2, the National Infrastructure for high-performance computing (HPC) and Data Storage in Norway and the HPC infrastructure IDUN at the NTNU.

FundersFunder number
Data Storage in Norway
Norges Teknisk-Naturvitenskapelige Universitet
Norges Forskningsråd275506
Norges Forskningsråd

    Keywords

    • Markov-chain Monte Carlo
    • asynchronous replica exchange
    • infinite swapping
    • path sampling
    • rare events

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