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Volume 149, Issue 11
21 September 2018
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Research Article| September 18 2018
Special Collection: Chemical Physics Software Collection
Mikkel Jørgensen
;
Mikkel Jørgensen a)
Department of Physics and Competence Centre for Catalysis, Chalmers University of Technology
, 412 96 Göteborg,
Sweden
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Henrik Grönbeck
Henrik Grönbeck b)
Department of Physics and Competence Centre for Catalysis, Chalmers University of Technology
, 412 96 Göteborg,
Sweden
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Author & Article Information
a)
Electronic mail: mikjorge@chalmers.se
b)
Electronic mail: ghj@chalmers.se
J. Chem. Phys. 149, 114101 (2018)
Article history
Received:
June 29 2018
Accepted:
August 31 2018
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Citation
Mikkel Jørgensen, Henrik Grönbeck; MonteCoffee: A programmable kinetic Monte Carlo framework. J. Chem. Phys. 21 September 2018; 149 (11): 114101. https://doi.org/10.1063/1.5046635
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Kinetic Monte Carlo (kMC) is an essential tool in heterogeneous catalysis enabling the understanding of dominant reaction mechanisms and kinetic bottlenecks. Here we present MonteCoffee, which is a general-purpose object-oriented and programmable kMC application written in python. We outline the implementation and provide examples on how to perform simulations of reactions on surfaces and nanoparticles and how to simulate sorption isotherms in zeolites. By permitting flexible and fast code development, MonteCoffee is a valuable alternative to previous kMC implementations.
Topics
Zeolites, Programming languages, Monte Carlo methods, Nanoparticle, Chemical elements, Adsorption, Reaction mechanisms, Reaction rate constants, Surface and interface chemistry, Catalysts and Catalysis
REFERENCES
1.
J. K. Nørskov T. Bligaard J. Rossmeisl C. H. Christensen
Nat. Chem.
1
,
37
(
2009
).
2.
M. J. Hoffmann M. Scheffler K. Reuter
ACS Catal.
5
,
1199
(
2015
).
3.
M. Stamatakis D. G. Vlachos
ACS Catal.
2
,
2648
(
2012
).
4.
D. T. Gillespie
J. Comput. Phys.
22
,
403
(
1976
).
5.
K. A. Fichthorn W. H. Weinberg
J. Chem. Phys.
95
,
1090
(
1991
).
6.
M. J. Hoffmann S. Matera K. Reuter
Comput. Phys. Commun.
185
,
2138
(
2014
).
7.
8.
S. S. Plimpton A. Thompson A. Slepoy
Spparks kinetic monte carlo simulator
,” accessed online 24 January 2018, http://spparks.sandia.gov/.
9.
M. Leetmaa N. V. Skorodumova
Comput. Phys. Commun.
185
,
2340
(
2014
).
10.
M. Leetmaa N. V. Skorodumova
Comput. Phys. Commun.
196
,
611
(
2015
).
11.
F. M. Kunz L. Kuhn O. Deutschmann
J. Chem. Phys.
143
,
044108
(
2015
).
12.
M. Stamatakis D. G. Vlachos
J. Chem. Phys.
134
,
214115
(
2011
).
13.
14.
S. R. Bahn K. W. Jacobsen
Comput. Sci. Eng.
4
,
56
(
2002
).
15.
A. P. J. Jansen
An Introduction to Kinetic Monte Carlo Simulations of Surface Reactions
(
Springer
,
Berlin
,
2012
), pp.
11
–
12
;
38
;
53
;
162
–
163
.
16.
A. Chatterjee A. F. Voter
J. Chem. Phys.
132
,
194101
(
2010
).
17.
M. Stamatakis D. G. Vlachos
Comput. Chem. Eng.
35
,
2602
(
2011
).
18.
E. C. Dybeck C. P. Plaisance M. Neurock
J. Chem. Theory Comput.
13
,
1525
(
2017
).
19.
See https://docs.python.org/2/tutorial/classes.html for a description of object oriented programming in python; accessed online 15 May 2018.
20.
M. Jørgensen H. Grönbeck
ACS Catal.
7
,
5054
(
2017
).
21.
M.
Jørgensen
andH.
Grönbeck
,Angew. Chem., Int. Ed.
57
,
5086
(
2018
).
22.
T. Nilsson Pingel M. Jørgensen A. Yankovich H. Grönbeck E. Olsson
Nat. Commun.
9
,
2722
(
2018
).
23.
P. Hohenberg W. Kohn
Phys. Rev.
136
,
B864
(
1964
).
24.
W. Kohn L. J. Sham
Phys. Rev.
140
,
A1133
(
1965
).
25.
See https://wiki.fysik.dtu.dk/ase/ase/atoms.html for a description of the ase atoms object; accessed online 15 June 2018.
26.
F. Calle-Vallejo J. I. Martínez J. M. García-Lastra P. Sautet D. Loffreda
Angew. Chem., Int. Ed.
53
,
8316
(
2014
).
27.
F. Calle-Vallejo D. Loffreda M. T. M. Koper P. Sautet
Nat. Chem.
7
,
403
(
2015
).
28.
B. Puchala M. L. Falk K. Garikipati
J. Chem. Phys.
132
,
134104
(
2010
).
29.
K. A. Fichthorn Y. Lin
J. Chem. Phys.
138
,
164104
(
2013
).
30.
31.
C. Paolucci A. A. Parekh I. Khurana J. R. D. Iorio H. Li J. D. Albarracin Caballero A. J. Shih T. Anggara W. N. Delgass J. T. Miller F. H. Ribeiro R. Gounder W. F. Schneider
J. Am. Chem. Soc.
138
,
6028
(
2016
).
32.
D. Cai Y. Ma Y. Hou Y. Cui Z. Jia C. Zhang
Catal. Sci. Technol.
7
,
2440
(
2017
).
33.
G. Kresse J. Furthmuller
Phys. Rev. B
54
,
11169
(
1996
).
34.
G. Kresse J. Furthmuller
Comput. Mater. Sci.
6
,
15
(
1996
).
35.
G. Kresse J. Hafner
Phys. Rev. B
47
,
558
(
1993
).
36.
G. Henkelman B. P. Uberuaga H. Jónsson
J. Chem. Phys.
113
,
9901
(
2000
).
37.
See http://theory.cm.utexas.edu for “Henkelman Group at the University of Texas at Austin Home Page” (accessed November 16, 2015).
38.
S. Smidstrup A. Pedersen K. Stokbro H. Jónsson
J. Chem. Phys.
140
,
214106
(
2014
).
39.
B. Hammer L. B. Hansen J. K. Nørskov
Phys. Rev. B
59
,
7413
(
1999
).
40.
J. Wellendorff K. T. Lundgaard A. Møgelhøj V. Petzold D. D. Landis J. K. Nørskov T. Bligaard K. W. Jacobsen
Phys. Rev. B
85
,
235149
(
2012
).
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2018
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