Multi-scale simulation methods - Fritz Haber Institute of the Max … · 2018-08-06 · Mie...
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Mie Andersen
Theoretical Chemistry, Technische Universität München
Multi-scale simulation methods
Hands-on DFT and beyond:
Frontiers of advanced electronic structure
and molecular dynamics methods
Peking University
August 6th, 2018
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2Mie Andersen | Multi-scale simulation methods
Outline
• Multi-scale modeling for catalysis, crystal growth and particle diffusion
• Ingredients of a multi-scale model
• Getting the processes and rate constants
• Kinetic Monte Carlo simulations
• Examples, challenges and current frontiers
o Sensitivity analysis
o Low-barrier problem
o Lateral interactions
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3Mie Andersen | Multi-scale simulation methods
Multi-scale modeling
Ab initio atomistic thermodynamics and statistical mechanics of surface properties and functions
K. Reuter, C. Stampfl, and M. Scheffler, in: Handbook of Materials Modeling Vol. 1,
(Ed.) S. Yip, Springer (Berlin, 2005). http://www.fhi-berlin.mpg.de/th/paper.html
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4Mie Andersen | Multi-scale simulation methods
Applications of multi-scale modeling
http://www.lce.hut.fi/publications/annual2000/node22.html
Crystal growth
Heterogeneous catalysis
Diffusion in battery materials
Urban et al., npj Computational Materials 2, 16002 (2016)
Reuter, K., in Modelling and Simulation of Heterogeneous Catalytic Reactions:
From the Molecular Process to the Technical System, O. Deutschmann, Editor.
2011, Wiley-VCH: Weinheim. p. 71-112
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5Mie Andersen | Multi-scale simulation methods
Ingredients of a multi-scale model
“Active site” model
IS FS
eq.
X
X
Rate constants
Process identification KMC simulation
Quantum Engine
{Ri} → Etot, Fi
Level of theory
x
xx
xx
x x x xxx
x
x
xxx
xx x
pCO (10-9 atm)
Exp.
Theory
0.0 1.0 2.0 3.06
4
2
0
TO
FC
O2
(10
12
mo
l/c
m2
s)
Model validation
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I. Active site model
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7Mie Andersen | Multi-scale simulation methods
Mind the materials gap !
Supported „real“ catalyst
Model catalysts
Courtesy: G. Rupprechter and Ch. Weiland, NanoToday 2, 20 (2007).
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8Mie Andersen | Multi-scale simulation methods
From single facets to nanoparticle models
Support
Nano-
particle
Common choice:
Limit study to one particular
site type
Reality:
• Bifunctional couplings
between different site types?
• Influence of support?
Reuter et al., J. Chem. Phys. 146, 040901 (2017)
Andersen et al., Angew. Chem. Int. Ed. 128, 1 (2016)
Jørgensen, Grönbeck, Angew. Chem. Int. Ed. 57, 5086 (2018)
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II. Quantum engine
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10Mie Andersen | Multi-scale simulation methods
Current workhorse: Density functional theory
XC functionals:
Practitioner level:
GGAs (metals)
Hybrids (molecules, insulators)
Major problems:
Self-interaction
(range-separated hybrids)
Van der Waals
(dispersion corrected functionals)
Have to expect inaccuracies in
binding energies/activation
barriers of order ~0.3-0.4 eV!
Jacob’s Ladder (Perdew et al.)
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III. Getting the rate constants
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12Mie Andersen | Multi-scale simulation methods
Rare event dynamics
E
IS
TS
FS
Brute force approach to rate constants:
i) Have accurate potential energy surface (forces)
ii) Run MD trajectory so long, that it establishes
equilibrium, crossing the barrier many, many
times back and forth:
k =no. of crossings IS FS per unit time
fraction of time system has spent in IS
require approximate theories to obtain process rates!
Yet:
- Relevant time step in MD run is fs (vibrations)
- Typical barrier E for surface reactions ~ 1 eV 10-2 - 102 reactions per second (TOF!)
- Requires to run trajectory over about 1015 – 1020 time steps unfeasable…
…and essentially 99,9999% of the time, the system will
just vibrate around IS basin (short time dynamics)
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13Mie Andersen | Multi-scale simulation methods
Transition state (activated complex) theory
Assumptions ( Eyring, Evans, Polanyi, ~1935 ):
i) Reaction system passes the barrier only once
(no recrossings)
ii) Energy distribution of reactant DOF is Boltzmann-
like (many collisions compared to reaction events
yield equilibrium between activated complex and IS,
except with respect to the reaction coordinate)
iii) Passage over barrier is the motion of only one DOF,
the reaction coordinate, which is independent of all
other motions of the activated complex (no concerted
motions)
iv) Passage over barrier is a classical event (no tunneling)
IS FS
eq.
X
X
Derivation: see e.g.
K.J. Laidler, Chemical kinetics,
Harper & Row, New York (1987) kTST = [ ( ) e S/k ] e-E/kTkT
hISFS
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IS
FS
TSx
x
xx x
xx
x
x
x
F true
F ||spring
x
14Mie Andersen | Multi-scale simulation methods
Transition state search: nudged elastic band - Initialize with several images {Ri} along a
straight-line interpolation
- Minimize
S(R1, …, RN) = i E(Ri) + i k/2 (Ri+1 - Ri )2
- Problem:
- elastic band cuts corners
- images tend to slide down towards
low-energy IS/FS regions, leaving few
images for relevant TS region
- Solution:
- only spring force component parallel
to path (no corner cutting)
- only true force component perpendicular
to path (no down-sliding)
widely applied workhorse
has problems, if energy varies largely along path,
but very little perpendicular to it (kinky PES)
G. Mills and H. Jónsson,
Phys. Rev. Lett. 72, 1124 (1994)
G. Henkelman, et al.,
J. Chem. Phys. 113, 9901 (2000)
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15Mie Andersen | Multi-scale simulation methods
Cheap barriers from BEP relations
Andersen et al., J. Chem. Phys. 147, 152705 (2017)Reaction coordinate [Å]
Energ
y [
eV
]
TS (expensive !)
IS (cheap !) FS (cheap !)
DFT-calculated
dissociation of H2O on
terrace site of Rh(211)
Activation e
nerg
y
Reaction energy
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16Mie Andersen | Multi-scale simulation methods
Cheap barriers from BEP relations
Reaction coordinate [Å]
Energ
y [
eV
]
TS (expensive !)
IS (cheap !) FS (cheap !)
DFT-calculated
dissociation of H2O on
terrace site of Rh(211)
Activation e
nerg
y
Reaction energy
Cu PdPtRhRu
Andersen et al., J. Chem. Phys. 147, 152705 (2017)
Brønsted-Evans-
Polanyi (BEP) relation
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17Mie Andersen | Multi-scale simulation methods
Cheap barriers from BEP relations
Reaction coordinate [Å]
Energ
y [
eV
]
TS (expensive !)
IS (cheap !) FS (cheap !)
DFT-calculated
dissociation of H2O on
terrace site of Rh(211)
Step site
of Rh(211)
Activation e
nerg
y
Reaction energy
Cu PdPtRhRu
Andersen et al., J. Chem. Phys. 147, 152705 (2017)
Brønsted-Evans-
Polanyi (BEP) relation
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18Mie Andersen | Multi-scale simulation methods
Cheap barriers from BEP relations
Reaction coordinate [Å]
Energ
y [
eV
]
TS (expensive !)
IS (cheap !) FS (cheap !)
DFT-calculated
dissociation of H2O on
terrace site of Rh(211)
Brønsted-Evans-
Polanyi (BEP) relation
Step site
of Rh(211)
Activation e
nerg
y
Reaction energy
Same BEP
relation obeyed !
Typical for
hydrogenation
Cu PdPtRhRu
Andersen et al., J. Chem. Phys. 147, 152705 (2017)
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19Mie Andersen | Multi-scale simulation methods
More BEP relationsN2 dissociation
Nørskov et al., Chem. Soc. Rev. 37, 2163 (2008)
Offset in β typical for
dissociation of strongly
bonded molecules !
Brønsted-Evans-Polanyi
(BEP) relation:
𝐸act. = 𝛼 ∙ Δ𝐸react. + 𝛽
Useful for estimating trends and limitations for transition metal catalyst activities !
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IV. Process identification
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21Mie Andersen | Multi-scale simulation methods
Diffusion at metal surfaces: Surprises…
Hopping mechanism
Ag(100) E = 0.45 eV
Au(100) E = 0.83 eV
B.D. Yu and M. Scheffler, Phys. Rev. B 56, R15569 (1997)
Exchange mechanism
Ag(100) E = 0.73 eV
Au(100) E = 0.65 eV
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22Mie Andersen | Multi-scale simulation methods
Automatized process identification
Hyperdynamics
Temperature accelerated dynamics
Accelerated molecular dynamics:
Other approaches: - parallel replica dynamics
- dimer method
…
A.F. Voter, F. Montalenti and T.C. Germann,
Annu. Rev. Mater. Res. 32, 321 (2002)
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V. Kinetic Monte Carlo simulations
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24Mie Andersen | Multi-scale simulation methods
Basics of KMC: Markovian state dynamics
Kinetic Monte Carlo
N
t
B
A
j
jij
j
ijii tPktPkdt
tdP)()(
)(
A
B
Molecular Dynamics
TS
kA→B
kB→A
ΔEA→B ΔEB→A
Reuter, K., in Modelling and Simulation of Heterogeneous Catalytic Reactions:
From the Molecular Process to the Technical System, O. Deutschmann, Editor.
2011, Wiley-VCH: Weinheim. p. 71-112
Chemical Master equation
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25Mie Andersen | Multi-scale simulation methods
KMC: essentially „coarse-grained MD“
Molecular Dynamics:
the whole trajectory
Kinetic Monte Carlo:
coarse-grained hops
ab initio MD:
up to 50 ps
ab initio KMC:
up to minutes
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26Mie Andersen | Multi-scale simulation methods
Flowchart of a kinetic Monte Carlo simulation
Determine all possible
processes p for given
system configuration and
build a list.
Get all rate constants
kp
Get two random numbers r1 , r2 ]0,1]
Calculate ktot = p kp
and find process “q”:
q q-1
kp r1 ktot kp
p=1 p =1
Execute process number “q”,
i.e. update configuration update clock
t t – ln(r2)/ktot
START
END
0
ktot
r1 ktotq
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VI. Simple toy model: Metal adatom diffusion
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28Mie Andersen | Multi-scale simulation methods
Au adatom diffusion: only hopping mechanism
Hopping: E = 0.83 eV
kTST = [ ( ) e S/k ] e-E/kT , T=300 KkT
hISFS
Initialize lattice (no particles -> deadlock)
X
simplification
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29Mie Andersen | Multi-scale simulation methods
Au adatom diffusion: only hopping mechanism
Hopping: E = 0.83 eV
kTST = [ ( ) e S/k ] e-E/kT , T=300 KkT
hISFS X
simplification
Initialize lattice with one particle
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30Mie Andersen | Multi-scale simulation methods
Au adatom diffusion: only hopping mechanism
𝑫 =𝒓 𝒕 − 𝒓𝟎
𝟐
𝟐𝒅𝒕Calculate diffusion coefficient by
tracking mean squared displacements
d is the lattice
dimension (2)
1 trajectory – stochastic noise !
∆Ehop = 0.83 eV
D = 0.32 Å2/s
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31Mie Andersen | Multi-scale simulation methods
Au adatom diffusion: only hopping mechanism
𝑫 =𝒓 𝒕 − 𝒓𝟎
𝟐
𝟐𝒅𝒕Calculate diffusion coefficient by
tracking mean squared displacements
d is the lattice
dimension (2)
Average over 50 trajectories
using different initialization
and random seed.∆Ehop = 0.83 eV
D = 0.34 Å2/s
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32Mie Andersen | Multi-scale simulation methods
Au adatom diffusion: include exchange mechanism
Hopping: E = 0.83 eV
kTST = [ ( ) e S/k ] e-E/kT , T=300 KkT
hISFS X
simplification
Exchange: E = 0.65 eV
Initialize lattice with one particle
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33Mie Andersen | Multi-scale simulation methods
Au adatom diffusion: include exchange mechanism
∆Ehop = 0.83 eV
D = 0.34 Å2/s
∆Ehop = 0.83 eV, ∆Eexc = 0.65 eV
D = 720 Å2/s
Garbage in – garbage out !
Overlooking the exchange mechanism affects the result by orders of magnitude.
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34Mie Andersen | Multi-scale simulation methods
How do DFT errors affect the result ?
Result is only sensitive to barriers for “rate-limiting” processes.
Trivial for 2-process model, but what about more complex (catalysis) models ?
∆Ehop = 0.83 eV
D = 0.34 Å2/s
∆Ehop = 0.83 eV, ∆Eexc = 0.65 eV
D = 720 Å2/s
∆Ehop = 0.73 eV, ∆Eexc = 0.65 eV
D = 760 Å2/s
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VII. Sensitivity analysis
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36Mie Andersen | Multi-scale simulation methods
More complex catalysis model
RuO
26 elementary processes (site-specific):
- O2 adsorption/desorption (dissociative/associative)
- CO adsorption/desorption (unimolecular)
- O and CO diffusion
- CO + O reaction
K. Reuter and M. Scheffler, Phys. Rev. B 73, 045433 (2006)
O
CO
K. Reuter, Oil&Gas Sci. Technol. 61, 471 (2006)
CO oxidation @ RuO2(110)
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Make use of ergodicity of KMC model to replace ensemble average by time average !
37Mie Andersen | Multi-scale simulation methods
KMC reaction rates
𝑅𝛽 =1
𝑡𝑓𝑖𝑛𝑎𝑙
𝑖=1
𝑁𝑘𝑀𝐶
𝒛
𝑘𝛽 𝒛 𝒚𝑖 Δ𝑡𝑖
𝑅𝛽: rate of reaction 𝛽, e.g. CO2 formation
𝑘𝛽: rate constant for CO2 formation
𝒚, 𝒛: initial and final lattice configuration for possible CO2 formation process
OCO
KMC time steps are discrete
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38Mie Andersen | Multi-scale simulation methods
Degree of rate control
• Measures how sensitive the reaction rate is
to a change of the rate constant of process i.
• Positive (negative) value means 𝑅𝛽increases (decreases) when 𝑘𝑖
+ increases.
𝑥𝑖𝛽+ =𝑘𝑖+
𝑅𝛽
𝜕𝑅𝛽
𝜕𝑘𝑖+𝑘𝑗≠𝑖+ ,𝑘𝑗
−
H. Meskine et al., Surf, Sci., 603, 1724 (2009)
Which processes are rate-
determining depends on the
reaction conditions !
Beyond finite difference sampling of
derivatives and global sensitivity analysis:
M. J. Hoffmann et al., J. Chem. Phys. 146, 044118 (2017)
S. Döpking et.al., Chem. Phys. Lett. 674, 28 (2017)
C. Stegelmann et al., JACS, 131, 8077 (2009)
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VIII. Low barrier (timescale disparity) problem
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40Mie Andersen | Multi-scale simulation methods
Timescale disparity problem
∆Ehop = 0.83 eV
D = 0.34 Å2/s
∆Ehop = 0.83 eV, ∆Eexc = 0.65 eV
D = 7.24 nm2/s
The timescales one can reach in a KMC simulation are limited by the fastest
process of the system.
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41Mie Andersen | Multi-scale simulation methods
CO methanation over stepped metalsReaction mechanism:
1) CO(g) + * ⇌ CO*
2) H2(g) + 2* ⇌ 2H*
3) CO* + H* ⇌ C* + OH*
4) C* + H* ⇌ CH* + *
5) CH* + H* ⇌ CH2* + *
6) CH2* + H* ⇌ CH3* + *
7) CH3* + H* ⇌ CH4(g) + 2*
8) OH* + H* ⇌ H2O(g) + 2*
9) OH* + OH* ⇌ H2O(g) + O* + *
10) O* + H* ⇌ OH* + *
11) Diffusion of all species
between all sites
← adsorption
← H-assisted CO dissociation
(only step sites are active)
← hydrogenation of C species
← hydrogenation of O species
Special H reservoir site
Metal(211) sites:
CO diffusion barrier ≈ 0.1 eV
CO dissociation barrier ≈ 1-2 eV
(metal dependent)
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42Mie Andersen | Multi-scale simulation methods
Acceleration of KMC simulations
Fast, quasi-equilibrated reaction channels are scaled with
equal scaling factors applied to the forward and reverse
processes to preserve thermodynamics (detailed balance):
𝑛+ − 𝑛−𝑛𝑒
< δ Dynamically partition processes into quasi-equilibrated and
non-equilibrated reaction channels:
ki+′ = αiki
+
ki−′ = αiki
−
αi ∈ ]0; 1]
Nf = r2 / r1
Dybeck et al., J. Chem. Theory Comput. 13, 1525 (2017)
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43Mie Andersen | Multi-scale simulation methods
Towards KMC-based catalyst screening
Use scaling relations (adsorption energies) and BEP relations (barriers) to express trends
over transition metal series in terms of only the C and O adsorption energies.
Traditional volcano plot computed
in the mean-field approximation
KMC volcano plot enabled by
acceleration algorithm
Andersen et al., J. Chem. Phys. 147, 152705 (2017)
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IX. Lateral interactions
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45Mie Andersen | Multi-scale simulation methods
Lateral interactions in KMC simulations
Cluster expansion model for interactions between species j
at site q and nearest neighbor species i at sites s:
can account for attractive / repulsive interactions, ordering and island formation
Straightforward generalization to many-body interactions and distant neighbors
high computational cost for complex interaction models
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46Mie Andersen | Multi-scale simulation methods
Interactions at DFT level
H-assisted CO dissociation:
Andersen et al. (in preparation)
Low coverage High coverage
Top view
Side view
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47Mie Andersen | Multi-scale simulation methods
Interactions at DFT level
Low
H-assisted CO dissociation:
High
Coverage
Cu Pd Pt Re Rh Ru
Low coverage High coverage
Top view
Side view
Andersen et al. (in preparation)
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48Mie Andersen | Multi-scale simulation methods
Interactions at DFT levelLow coverage High coverage
Top view
Side view
Low
H-assisted CO dissociation:
High
Coverage
Cu Pd Pt Re Rh Ru
Andersen et al. (in preparation)
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49Mie Andersen | Multi-scale simulation methods
Interactions at KMC level
Standard KMC implementation:
• Every neighbour configuration is treated as a
separate process.
• Cost grows linearly with number of processes
-> exponentially with number of interactions.
Seibt et al. (in preparation)
standardCO oxidation on RuO2(110) surface
Reuter et al., Phys. Rev. B 73, 045433 (2006)
? ?
?
? ?
?
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50Mie Andersen | Multi-scale simulation methods
Interactions at KMC level
“on-the-fly” implementation:
• Rate constants of processes affected by lateral
interactions are calculated “on-the-fly” instead of
at model initiation.
standard
on-the-fly
CO oxidation on RuO2(110) surface
? ?
?
? ?
?
Seibt et al. (in preparation)
Reuter et al., Phys. Rev. B 73, 045433 (2006)
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51Mie Andersen | Multi-scale simulation methods
Interactions at KMC level
standard
on-the-fly
10 × 10
lattice
50 × 50
CO oxidation on RuO2(110) surface
…
? ?
?
? ?
?
Seibt et al. (in preparation)
Reuter et al., Phys. Rev. B 73, 045433 (2006)
“on-the-fly” implementation:
• Rate constants of processes affected by lateral
interactions are calculated “on-the-fly” instead of
at model initiation.
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52Mie Andersen | Multi-scale simulation methods
Interactions at KMC level
standard
on-the-fly
10 × 10
lattice
50 × 50
CO oxidation on RuO2(110) surface
…
? ?
?
? ?
?
In progress:
Combining the “on-the-fly”
implementation with the
acceleration algorithm.
Seibt et al. (in preparation)
Reuter et al., Phys. Rev. B 73, 045433 (2006)
“on-the-fly” implementation:
• Rate constants of processes affected by lateral
interactions are calculated “on-the-fly” instead of
at model initiation.
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All models were realized using the “kmos” software package:
Development: https://github.com/mhoffman/kmos
Documentation: http://kmos.readthedocs.io/
Mailing list: https://groups.google.com/group/kmos-users/
Further tutorials (incl. crystal growth models): https://github.com/jmlorenzi/intro2kmos
53Mie Andersen | Multi-scale simulation methods
“kmos” KMC code
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54Mie Andersen | Multi-scale simulation methods
Acknowledgements & developers
Karsten Reuter
(TU Munich)
Max Hoffmann
(Stanford Uni.)Sebastian Matera
(Freie Uni., Berlin)Juan Lorenzi
(Siemens)
Mie Andersen
(TU Munich)
Andy Garhammer
(Siemens)
Michael Seibt
(TU Munich)