Computational molecular engineering: Scalable simulation and - - PowerPoint PPT Presentation

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Computational molecular engineering: Scalable simulation and - - PowerPoint PPT Presentation

Aug 05, 2023 17 likes •248 views

Laboratory of Engineering Thermodynamics (LTD) Prof. Dr.-Ing. H. Hasse Computational molecular engineering: Scalable simulation and reliable modelling Martin Horsch Laboratory of Engineering Thermodynamics, University of Kaiserslautern

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Computational molecular engineering: Scalable simulation and reliable modelling

Martin Horsch Laboratory of Engineering Thermodynamics, University of Kaiserslautern Tianjin, 16th November 2015 Tianjin Center for Applied Mathematics

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Computational molecular engineering

From Physics (qualitative accuracy) To Engineering (quantitative reliability)

  • Physically realistic modelling of

intermolecular interactions

  • Separate contributions due to

repulsive and dispersive as well as electrostatic interactions

  • No blind fitting, but parameters of

effective pair potentials are adjusted to experimental data

  • Physical realism facilitates reliable

interpolation and extrapolation

2 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Geometry Bond lengths and angles

Force fields for molecular modelling

Electrostatics Point polarities (charge, dipole, quadrupole): Position and magnitude Dispersion and repulsion Lennard-Jones potential: Size and energy parameters

3 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Simulation of bulk properties with ms2

10-3 ρ / mol m-3

20 25 30 35

104 ηs / Pa s

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

N2 O2 CO2

Shear viscosity

10-3 ρ / mol m-3 10-3 ρ / mol m-3

5 10 15 20 25

104 Dρ / mol m

  • 1s-1

1 2 3 4 5 CO2 C2H6 C2H4

Self-diffusion coefficient

10-3 ρ / mol m-3

10-3 ρ / mol m-3

20 22 24 26 28 30

102 λ / Wm-1K-1

5 10 15 20 Thermal conductivity

10-3 ρ / mol m-3 102 λ / W m-1 K-1

Second virial coefficient

B / cm3 mol-1 T / K

ms2 is freely available for academic use – register at http://www.ms-2.de/

4 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Efficient simulation of large systems

Methods for heterogeneous

  • r fluctuating particle

distributions: Linked-cell data structure suitable for spatial domain decomposition:

(non-blocking, over- lapping MPI send/ receive operations)

large systems “1”: molecular dynamics http://www.ls1-mardyn.de/

5 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

large systems “1”: molecular dynamics http://www.ls1-mardyn.de/

Efficient simulation of large systems

Optionally, forces acting on molecules are only stored until their cell leaves the sliding window. hyperthreaded sliding window Memory-efficient implementation based on the linked-cell data structure: Efficient vectorization:

  • Optimization by hand, using advanced vector extensions (AVX).
  • Conversion from array of structures (AoS) to structure of arrays (SoA).

6 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Large-scale MD simulation on SuperMUC

Scaling of ls1 mardyn examined on up to 146 016 cores, i.e. the whole SuperMUC at the Leibniz Supercomputing Centre, Garching, in 2013. i d e a l s t r

  • n

g s c a l i n g

  • bserved strong scaling

number of cores speedup (relative to 128 cores) homogeneous LJTS liquid with 4.8 billion molecules

7 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Up to N = 4 · 1012

  • n SuperMUC

number of cores speedup

2013

weak scaling with 31.5 million molecules per core large systems “1”: molecular dynamics http://www.ls1-mardyn.de/

Large-scale MD simulation on SuperMUC

8 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Large-scale MD simulation on hermit

homogeneous cavitation

CO2 (T = 280 K and ρ = 17.2 mol/l), 3CLJQ 100 million interaction sites, 110 592 cores

9 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Molecular simulation of fluids at interfaces

  • Vapour-liquid surface tension
  • Nucleation and dispersed phases
  • Adsorption (fluid-fluid and fluid-solid)
  • Contact angle and contact line pinning

LJTS T = 0.8 ε θpl = 90° local fluid density

10 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Long-range correction at planar interfaces

Full evaluation of all pairwise interactions is too expensive ... ... short-range interactions are evaluated only for neighbours. For planar interfaces: Long-range correction from the density profile, following Janeček. short range (explicit) long range (correction) cutoff radius

11 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

For planar interfaces: Long-range correction from the density profile, following Janeček.

Long-range correction at planar interfaces

Angle-averaging expression for multi-site models, following Lustig. Two-centre LJ fluid (2CLJ) Janeček-Lustig term no angle averaging no long-range correction 1 nm cutoff radius / σ surface tension / εσ -2 T = 0.979 ε Dipole and dispersion lead to analogous long-range correction expressions. The long-range contribution of the quadrupole can be neglected.

12 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Surface tension at high precision

temperature / ε surface tension / εσ -2 Lennard-Jones fluid

1.23 2 c

( ) 2.94 1 T T T ε γ σ   = −  ÷  

LJ

13 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Objective: Accuracy for multiple properties

uncertainty

  • f reference

molecular model

ethylene oxide model by Eckl et al. (2008)

14 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Force fields for molecular modelling

simulation DIPPR correlation vapour pressure (logarithmic) temperature density [mol/l] inverse temperature [1/K]

No interfacial properties were considered for the parameterization. Fit of parameters σ, ε, L, Q to VLE data of 29 fluids by Stoll et al. Deviation:

  • δρ' ≈ 1 %
  • δP sat ≈ 5 %

2CLJQ models:

  • 2 LJ centres
  • Quadrupole

15 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Predictive capacity of literature models

Two LJ + quadrupole (2CLJQ) Two LJ + dipole (2CLJD) Fit to bulk properties 10 to 20 % overestimation of vapour-liquid surface tension

16 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Massively parallel molecular modelling

temperature / ε

L* = 0.2

Q* = 1.41

L* = 0.6

Q* = 1.41

L* = 0.4

Q* = 2

L* = 0.4

Q* = 0

L* = 0.4

Q* = 1.41

surface tension / εσ -2 Two LJ + dipole (2CLJD) Two LJ + quadrupole (2CLJQ)

  • Systematic exploration of the four-dimensional model parameter space
  • Correlation of the surface tension by a critical scaling expression

17 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Multicriteria model optimization

Multicriteria optimization requires massively-parallel molecular modelling. Pareto set for carbon dioxide Pareto optimality criterion

18 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Pareto sets for 2CLJQ models of real fluids

Projections of the Pareto set on the parameter space reveal intrinsic cor- relations between different model parameters, such as ε and Q.

criteria: ρ', ps, γ

19 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Pareto sets for 2CLJQ models of real fluids

The dimension of the parameter space is effectively reduced, facilitating an efficient multicriteria optimization by navigating on the Pareto set.

criteria: ρ', ps, γ

20 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Representation of objective and parameter spaces by patch plots:

Pareto sets for 2CLJQ models of real fluids

Pareto-optimal 2CLJQ models for molecular oxygen

21 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Fast and simple model parameterization

22 16th November 2015 Martin Horsch

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Laboratory of Engineering Thermodynamics (LTD)

  • Prof. Dr.-Ing. H. Hasse

Summary

The traditional art of molecular modelling An expert modelling artist designs and publishes

  • a single optimized model for a particular fluid,
  • according to his choice of criteria (often unknown to the public),
  • users are passive, they have to live with the artists' decision.

Scientific modelling by multicriteria optimization For established model classes and multiple thermodynamic criteria,

  • the dependence of thermodynamic properties on the model

parameters is determined and correlated,

  • the deviation between model properties and real fluid behaviour

is characterized, and the Pareto set is published,

  • users can design their own tailored model with minimal effort.

23 16th November 2015 Martin Horsch