Accelerating Molecular Dynamics Simulations Using Socket-Based Interprocess Communication

Molecular dynamics (MD) simulations are essential for studying the time evolution of molecular systems. Still, their efficiency is often bottlenecked by file-based interprocess communication (IPC) between MD and electronic structure programs. We present a socket-based IPC implementation that dramatically accelerates MD simulations, reducing the computational time by >10-fold compared to those of traditional file-based methods. Our approach, applied to nonadiabatic molecular dynamics with the Newton-X program, eliminates disk read/write overhead, allowing for faster simulations over longer time scales. This method opens the door to more efficient high-throughput simulations, providing new opportunities for exploring complex molecular processes in real time.

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Publication Details

Journal
The Journal of Physical Chemistry Letters
Published
2024-11-21
DOI
https://doi.org/10.1021/acs.jpclett.4c02860
Citations
2
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.23

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article

Accelerating Molecular Dynamics Simulations Using Socket-Based Interprocess Communication

Matheus de Oliveira Bispo, Mario Barbatti
2 citations
The Journal of Physical Chemistry Letters
Machine Learning in Materials Science
0.23
article

Accelerating Molecular Dynamics Simulations Using Socket-Based Interprocess Communication

Matheus de Oliveira Bispo, Mario Barbatti
article en
2 citations

Abstract

Molecular dynamics (MD) simulations are essential for studying the time evolution of molecular systems. Still, their efficiency is often bottlenecked by file-based interprocess communication (IPC) between MD and electronic structure programs. We present a socket-based IPC implementation that dramatically accelerates MD simulations, reducing the computational time by >10-fold compared to those of traditional file-based methods. Our approach, applied to nonadiabatic molecular dynamics with the Newton-X program, eliminates disk read/write overhead, allowing for faster simulations over longer time scales. This method opens the door to more efficient high-throughput simulations, providing new opportunities for exploring complex molecular processes in real time.

The Journal of Physical Chemistry LettersVol. 15(47)
Centre National de la Recherche Scientifique (FR), Institut Universitaire de France (FR), Aix-Marseille Université (FR), Institut de Chimie Radicalaire (FR)
Aix-Marseille Université, Fondation Aix-Marseille Universite, H2020 European Research Council
Affordable and clean energy
Openalex Percentile: Top 50%
Machine Learning in Materials Science
0.23
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