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DL[formula omitted]POLY 5: Calculation of system properties on the fly for very large systems via massive parallelism.

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Title: DL[formula omitted]POLY 5: Calculation of system properties on the fly for very large systems via massive parallelism.
Authors: Devereux, H.L.1 (AUTHOR) h.devereux@qmul.ac.uk, Cockrell, C.1,2 (AUTHOR), Elena, A.M.3 (AUTHOR), Bush, Ian4,5 (AUTHOR), Chalk, Aidan B G6 (AUTHOR), Madge, Jim3,7 (AUTHOR), Scivetti, Ivan3 (AUTHOR), Wilkins, J.S.4,5 (AUTHOR), Todorov, I.T.1,3 (AUTHOR), Smith, W.3 (AUTHOR), Trachenko, K.1 (AUTHOR)
Source: Computer Physics Communications. Jun2026, Vol. 323, pN.PAG-N.PAG. 1p.
Subjects: Molecular dynamics, Real-time computing, Trajectories (Mechanics), Statistical correlation, Parallel programming, High performance computing
Abstract: Modelling has become a third distinct line of scientific enquiry, alongside experiments and theory. Molecular dynamics (MD) simulations serve to interpret, predict and guide experiments and to test and develop theories. A major limiting factor of MD simulations is system size and in particular the difficulty in handling, storing and processing trajectories of very large systems. This limitation has become significant as the need to simulate large system sizes of the order of billions of atoms and beyond has been steadily growing. Examples include interface phenomena, composite materials, biomaterials, melting, nucleation, atomic transport, adhesion, radiation damage and fracture. More generally, accessing new length and energy scales often brings qualitatively new science, but this has currently reached a bottleneck in MD simulations due to the traditional methods of storing and post-processing trajectory files. To address this challenge, we propose a new paradigm of running MD simulations: instead of storing and post-processing trajectory files, we calculate key system properties on-the-fly. Here, we discuss the implementation of this idea and on-the-fly calculation of key system properties in the general-purpose MD code, DL _ POLY. We discuss code development, new capabilities and the calculation of these properties, including correlation functions, viscosity, thermal conductivity and elastic constants. We give examples of these on-the-fly calculations in very large systems. Our developments offer a new way to run MD simulations of large systems efficiently in the future. Program Title: DL _ POLY_5 CPC Library link to program files: https://doi.org/10.17632/dfy3tr4rrz.1 Developer's repository link: https://gitlab.com/ccp5/dl-poly Licensing provisions: L-GPL v3.0 Programming language: Fortran 2008 Nature of problem: Molecular dynamics is utilised for modelling in many domains including physics, chemistry, biology, materials science, and their interfaces. These applications often call for large scale simulations targeting high-fidelity timescales and ever larger length scales. In all these cases efficient use of computation, storage, and input/output (I/O) load handling on both the software and hardware sides is required to facilitate analysis. Solution method: DL_POLY provides an efficient set of algorithms for molecular simulation alongside a domain-decomposition strategy to distributed computation efficiently across massively parallel CPU systems. These include parallel I/O handling, link-cells, smooth particle mesh Ewald electrostatics, and now a general purpose on-the-fly correlation framework. This latter development addresses the problem of storing and analysing large trajectory files by iteratively computing correlations at runtime. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:Modelling has become a third distinct line of scientific enquiry, alongside experiments and theory. Molecular dynamics (MD) simulations serve to interpret, predict and guide experiments and to test and develop theories. A major limiting factor of MD simulations is system size and in particular the difficulty in handling, storing and processing trajectories of very large systems. This limitation has become significant as the need to simulate large system sizes of the order of billions of atoms and beyond has been steadily growing. Examples include interface phenomena, composite materials, biomaterials, melting, nucleation, atomic transport, adhesion, radiation damage and fracture. More generally, accessing new length and energy scales often brings qualitatively new science, but this has currently reached a bottleneck in MD simulations due to the traditional methods of storing and post-processing trajectory files. To address this challenge, we propose a new paradigm of running MD simulations: instead of storing and post-processing trajectory files, we calculate key system properties on-the-fly. Here, we discuss the implementation of this idea and on-the-fly calculation of key system properties in the general-purpose MD code, DL _ POLY. We discuss code development, new capabilities and the calculation of these properties, including correlation functions, viscosity, thermal conductivity and elastic constants. We give examples of these on-the-fly calculations in very large systems. Our developments offer a new way to run MD simulations of large systems efficiently in the future. Program Title: DL _ POLY_5 CPC Library link to program files: https://doi.org/10.17632/dfy3tr4rrz.1 Developer's repository link: https://gitlab.com/ccp5/dl-poly Licensing provisions: L-GPL v3.0 Programming language: Fortran 2008 Nature of problem: Molecular dynamics is utilised for modelling in many domains including physics, chemistry, biology, materials science, and their interfaces. These applications often call for large scale simulations targeting high-fidelity timescales and ever larger length scales. In all these cases efficient use of computation, storage, and input/output (I/O) load handling on both the software and hardware sides is required to facilitate analysis. Solution method: DL_POLY provides an efficient set of algorithms for molecular simulation alongside a domain-decomposition strategy to distributed computation efficiently across massively parallel CPU systems. These include parallel I/O handling, link-cells, smooth particle mesh Ewald electrostatics, and now a general purpose on-the-fly correlation framework. This latter development addresses the problem of storing and analysing large trajectory files by iteratively computing correlations at runtime. [ABSTRACT FROM AUTHOR]
ISSN:00104655
DOI:10.1016/j.cpc.2026.110128