HylleraasMD: A Domain Decomposition-Based Hybrid Particle-Field Software for Multi-Scale Simulations of Soft Matter

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


We present HylleraasMD (HyMD), a comprehensive implementation of the recently proposed Hamiltonian formulation of hybrid particle-field molecular dynamics (hPF). The methodology is based on tunable, grid-independent length-scale of coarse graining, obtained by filtering particle densities in reciprocal space. This enables systematic convergence of energies and forces by grid refinement, also eliminating non-physical force aliasing. Separating the time integration of fast modes associated with internal molecular motion, from slow modes associated with their density fields, we implement the first time-reversible hPF simulations. HyMD comprises the optional use of explicit electrostatics, which, in this formalism, corresponds to the long-range potential in Particle-Mesh Ewald. We demonstrate the ability of HhPF to perform simulations in the microcanonical and canonical ensembles with a series of test cases, comprising lipid bilayers and vesicles, surfactant micelles, and polypeptide chains, comparing our results to established literature. An on-the-fly increase of the characteristic coarse graining length significantly speeds up dynamics, accelerating self-diffusion and leading to expedited aggregation. Exploiting this acceleration, we find that the time scales involved in the self-assembly of polymeric structures can lie in the tens to hundreds of picoseconds instead of the multi microsecond regime observed with comparable coarse-grained models.


self assembly
density functional

Supplementary materials

Supporting information for the manuscript
HhPF interaction energy parameters for DPPC, DOPC, Lipid A, AzoTMA, and the model Alanine peptide; coarse-grained mapping for AzoTMA;and DPPC5 momentum conservation.


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