DL_FFLUX: a parallel, quantum chemical topology force field

22 June 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


DL_FFLUX performs molecular dynamics for flexible molecules endowed with polarisable QTAIM atomic multipole moments, predicted by the machine learning method Gaussian Process Regression. Newly optimised and parallelised using domain-decomposition MPI, DL_FFLUX now operates in reasonable time frames. DL_FFLUX is delivered as an add-on to the widely distributed molecular dynamics code DL_POLY 4.08. For the systems studied here (10**3-10**5 atoms), DL_FFLUX adds minimal computational cost to the standard DL_POLY package. The parallel DL_FFLUX preserves the quality of the scaling of the MPI implementation in standard DL_POLY. For the first time it is feasible to use the full capability of DL_FFLUX to study systems that are large enough to be of real world interest. For example, a fully flexible, high-rank polarised (up to and including quadrupole moments) 1 ns simulation of a system of 10,125 atoms (3,375 water molecules) takes 30 hours (wall time) on 18 cores.


machine learning
multipole moments
liquid water
MD simulation
Quantum Chemical Topology
Multipole moments
gaussian process regression

Supplementary materials

FFLUX: a parallel, quantum chemical topology force field
Section 1 on weak scaling contains Figures S1‐S4 showing runtime as a function of the number of atoms for varying L’ and Np values. Section 2 on the cut‐off radius gives timings in Table S1. Section 3 on L’ shows Figure S5 with timings as a function of L’ and the number of atoms. Section 4 on scaling contains Figures S6, S7 and S8, with relative speed‐ups as a function of L’ and an increasing number of atoms. Section 5 on profiling shows Figures S9 and S10 with pie charts, and Figure S11 with the fraction of total runtime spent in MPI communications as a function of Np. Section 6 provides technical details on optimisation.


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