Adaptively Restrained Molecular Dynamics in LAMMPS · Adaptively Restrained Molecular Dynamics in...
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Adaptively Restrained Molecular Dynamics in LAMMPS
Svetlana ARTEMOVA, Semeho EDORH, Dmitriy MARIN, Krishna SINGH, Zofia TRSTANOVA and Stephane REDON
- 1 CECAM-RA LAMMPS 26/06/2018
By Semeho EDORH
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Classical MD simulations
Generate a temporal evolution (Positions
and momenta) of a particle system
Retrieve macroscopic information by
averaging properties along trajectories
Expensive task due to force
computations, especially for pairwise non-bonded forces
Most interaction forces are computed
from the scratch at each timestep.
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Adaptively Restrained MD simulations
ARMD freezes at each timestep some
particles
Although the phase space is explored
differently, several properties are
preserved
Computationally less expensive when
interaction forces depend upon
interatomic distances
Interaction forces can be incrementally updated at each timestep.
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ARMD: How it works?
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[Artemova and Redon 2012]
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ARMD: The Hamiltonian
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ARMD: Some properties
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[Trstanova 2016]
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ARMD in LAMMPS
NVE ensemble: Fix_ARMD_NVE.cpp
NVT Langevin: Fix_ARMD_Langevin.cpp
Integrator: ARMD.cpp
Incremental algorithms for non-bonded pairwise interactions
General treatment for short-range interactions
Specialized algorithms for electrostatics
ARMD in KOKKOS package
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Short-range potentials
Several pair_***.cpp in LAMMPS
Van der Walls potentials (Lennard
Jones, Buckingham, etc.)
Can be truncated beyond a certain
cutoff
Efficiently computed with Neighbor Lists
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Incremental Algorithms for short-range interactions
Restrained Interactions between restrained-restrained particles
Active Interactions which involve at least one active
particle (active-restrained, active-active)
Force decomposition:
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[Edorh 2018]
[Singh 2017]
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Active Neighbor lists
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[Singh 2017]
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Benchmark: NVT simulation of LJ particles
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[Singh 2017]
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Benchmark: NVT simulation of LJ particles + MPI
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MPI enabled
864K particles
8/16 CPUs Intel Xeon E5540 per node
Gigabit Ethernet network
[Marin 2017]
Breakdown of wall-clock time for 1 and 4 nodes with 4 processes per each node normalized by LAMMPS timing for different percentage of restrained particles Other – Load balancing, ARMD routines
for switched particles (ANL and force computations of switched particles), position & momenta update; comm – communications; neig – neighbor list
construction; force – force computation
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ARMD and KOKKOS
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Nano-projectile: Cu1000
(r = 1.415 nm)
Substrate: Cu, 677K atoms
(L = 25 nm; H = 12.5 nm)
Nano-projectile velocity: 5 km/sec
(8.25 eV per atom, total: 8.25 keV)
Truncated Lennard-Jones potential
Neighbor list update each 20
timesteps
7
Benchmark: nano-projectile impact on surface
[Marin 2018]
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Parameters εr, εf are in eV.
Nano-projectile: 8.25 eV per atom
Slice: 1 nm
Benchmark: nano-projectile impact on surface
ARMD, εr = 1, εf = 2 Classical MD ARMD, εr = 3, εf = 4
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Benchmark: nano-projectile impact on surface
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t = 2 ps t = 4 ps t = 10 ps t = 15 ps
AR
MD
, ε r
= 1
, ε f
= 2
C
lass
ical M
D
AR
MD
, ε r
= 3
, ε f
= 4
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The cluster volume as a number of missing atoms in the substrate.
Classical MD
Benchmark: nano-projectile impact on surface
Sp
eed
up
x1
.84
S
peed
up
x2
.35
Sp
eed
up
x2
.0
Sp
eed
up
x2
.54
Sp
eed
up
x2
.42
[Marin 2018]
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Incremental algorithms for Electrostatics
Long-range interactions (Electrostatics) are
extremely expensive
Electrostatics are efficiently handled by splitting
short- and long- range contributions:
Pair.cpp + kspace.cpp They can also benefit from adaptive restraints
Short-range terms are enhanced by ANLs
Long-range terms require more specific
algorithms
Improved algorithms
Ewald summation
P3M
Meshed continuum method
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Ewald summation
Coulomb point charges are smeared with Gaussians
Forces are split into Real- and Fourier- spaces contributions:
Real space :
K-space :
Structure factor :
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Incremental Ewald summation
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Incremental Ewald summation
Good acceleration but still slower than P3M
Speedup as function of the proportion of restrained particles.
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Particle Particle Particle Mesh (P3M)
Ewald summation… but long-range terms are treated on a grid
Charges are sampled on a mesh
Electric field/Coulomb potential is retrieved with Fast Fourier
Transforms
Forces are interpolated from the electric field
Grid size and sigma controls accuracy/speed!
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Incremental Particle Particle Particle Mesh (IP3M)
Divide and Conquer
Grid sizes can be linked to the accuracy of
computations
Electric field is evaluated at atomic positions
Problem 1 can be solved once.
Valid only when particles can’t switch state
Problem 1 Problem 2
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- 24 CECAM-RA LAMMPS 26/06/2018
Incremental Particle Particle Particle Mesh (IP3M)
We can also handle switching particles Error in forces (x-direction) of incremental Particle
Particle Particle Mesh
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- 25 CECAM-RA LAMMPS 26/06/2018
Incremental Particle Particle Particle Mesh (IP3M)
Evolution of the RMS force error of IP3M. 90% of particles are restrained 1% of particles switch at each timestep.
Speedup of long- (resp. short-) range calculations 1.35 (resp. 3.9).
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Meshed Continuum Method (MCM)
The grid size and the support of the density control accuracy/speed!
[Bolten 2008, Arnold 2013]
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- 27 CECAM-RA LAMMPS 26/06/2018
Incremental Meshed Continuum Method ( IMCM)
Sampling of the RHS can benefit from a divide and conquer
The Particle-Particle near-field correction is treated with pair_***.cpp : ANLs
IMCM outperforms P3M for low numbers of active particles.
[Edorh 2018]
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Application : Polyelectrolyte translocation
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- 29 CECAM-RA LAMMPS 26/06/2018
Application : Polyelectrolyte translocation
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Future work
Incremental Multilevel Summation Method
Improve performance on multicore architectures
Applications such as channels in membrane proteins, ion implantation,
molecular docking, protein folding
Study of correlated motions (Essential dynamics using ARMD)
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Publications
Svetlana Artemova and Stephane Redon. Adaptively restrained particle
simulations. Physical Review Letters, 109(19):190201, nov 2012. ISSN 00319007.
doi: 10.1103/PhysRevLett.109.190201.
Zofia Trstanova and Stephane Redon. Estimating the speed-up of adaptively restrained Langevin dynamics. Journal of Computational Physics, 336:412–
428,2017.
Gabriel Stoltz and Zofia Trstanova. Stable and accurate schemes for langevin dynamics with general kinetic energies. arXiv preprint arXiv:1609.02891, 2016.
Krishna Kant Singh and Stephane Redon. Adaptively Restrained Molecular Dynamics in LAMMPS. Modelling and Simulation in Materials Science and
Engineering, 2017. ISSN 1361651X. doi: 10.1088/1361-651X/aa7345.
Krishna Kant Singh, Dmitriy F. Marin, and Stephane Redon. Parallel adaptively restrained molecular dynamics. In High Performance Computing &
Simulation(HPCS), 2017 International Conference on, pages 308–314. IEEE, 2017.
Krishna Kant Singh and Stephane Redon. Single-pass incremental force updates for adaptively restrained molecular dynamics. Journal of Computational
Chemistry, 2017. ISSN 1096-987X. doi: 10.1002/jcc.25126.
Semeho Prince A. Edorh and Stéphane Redon. Incremental update of electrostatic interactions in adaptively restrained particle simulations. Journal
of Computational Chemistry, 2018. doi: 10.1002/jcc.25215.
Dmitriy F. Marin, and Stephane Redon. Parallel implementation of Adaptively Restrained Molecular Dynamics in LAMMPS using Kokkos. The International
Journal of High Performance Computing Applications,(in review), 2018.
https://team.inria.fr/nano-d/