DL_POLY 5: Calculation of system properties on the fly for very large systems via massive parallelism

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.

Figures (13)

• Figure 1: A representative 0.5 MeV collision cascade from our earlier work Zarkadoula_2013 showing displaced atoms at 0.2, 1.5 ps and 100 ps. The system size is a cube of 2000 Å size with 0.1 billion atoms. Animations showing the propagation of the collision cascade can be watched from cascade.
• Figure 2: Viscosity for supercritical Argon, as calculated using DL$\_$POLY's on-the-fly correlations. In each case experimental data from NIST is compared to data from $N=500$ atom simulations averaged over $20$ simulations, and large scale simulations with $N=10^{8}$ atoms averaged over $5$ simulations, also compared to NIST nist
• Figure 3: Thermal conductivity for supercritical Argon, as calculated using DL$\_$POLY's on-the-fly correlations. In each case experimental data from NIST is compared to data from $N=500$ atom simulations averaged over $20$ simulations, and large scale simulations with $N=10^{8}$ atoms averaged over $5$ simulations, also compared to NIST nist. See also figure \ref{['fig:viscosity']}.
• Figure 4: Elastic constants for FCC Argon, as calculated by DL$\_$POLY compared to previous simulation results. In general there is good agreement with data that exists, notably existing data is quite sparse. Our data is averaged over $n=20$ initial conditions, and statistics collected over $T=10^6$ steps, with a $10$ ps maximum correlation lag time.
• Figure 5: The bulk and shear modulus, derived from DL$\_$POLY elastic constants (see Fig \ref{['fig:sims-comp']} for the raw data), compared with experimental data. Voigt and Reuss averaging give upper and lower bounds on the shear modulus respectively as expected. The Bulk modulus is also in good agreement Like pereverzev2022isothermal we find a linear relationship in $C_{11}$ and $C_{12}$ with temperature and hence the same is true for $B$.