FMD: A parallel library for performing classical molecular dynamics simulations

In this paper, the MPI-based parallel library Free Molecular Dynamics (FMD) is introduced. The general structure of a typical program utilizing FMD to perform classical molecular dynamics (MD) simulation is described. Much of the paper is devoted to one of the advanced features of FMD: its ability to compute physical fields defined on distributed grids (a.k.a. “turies”). Each cell of a distributed grid may be shared among any number of MPI processes. Distributed grids can be used to combine MD with continuum models or to obtain macroscopic quantities, e.g. density, as functions of time and space. The library is designed to provide an easy to use interface for its users, as well as equip its developers with reusable tools required for expanding the code.

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Publication Details

Journal
SoftwareX
Published
2024-11-05
DOI
https://doi.org/10.1016/j.softx.2024.101929
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
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article

FMD: A parallel library for performing classical molecular dynamics simulations

Arham Amouye Foumani
SoftwareX
Machine Learning in Materials Science
article

FMD: A parallel library for performing classical molecular dynamics simulations

Arham Amouye Foumani
article en

Abstract

In this paper, the MPI-based parallel library Free Molecular Dynamics (FMD) is introduced. The general structure of a typical program utilizing FMD to perform classical molecular dynamics (MD) simulation is described. Much of the paper is devoted to one of the advanced features of FMD: its ability to compute physical fields defined on distributed grids (a.k.a. “turies”). Each cell of a distributed grid may be shared among any number of MPI processes. Distributed grids can be used to combine MD with continuum models or to obtain macroscopic quantities, e.g. density, as functions of time and space. The library is designed to provide an easy to use interface for its users, as well as equip its developers with reusable tools required for expanding the code.

SoftwareXVol. 28
Openalex Percentile: Top 88%
Machine Learning in Materials Science
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