kmos: A lattice kinetic Monte Carlo framework

被引:95
作者
Hoffmann, Max J. [1 ]
Matera, Sebastian
Reuter, Karsten
机构
[1] Tech Univ Munich, Chair Theoret Chem, D-85747 Garching, Germany
关键词
Lattice kinetic Monte Carlo; Microkinetic modeling; First-principles multi-scale modeling; Heterogeneous catalysis; Graphical user interface; !text type='Python']Python[!/text; Fortran90; Open source; SIMULATION; MECHANISM; HYDROGENATION; !text type='PYTHON']PYTHON[!/text; SCALE; TOOL;
D O I
10.1016/j.cpc.2014.04.003
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Kinetic Monte Carlo (kMC) simulations have emerged as a key tool for microkinetic modeling in heterogeneous catalysis and other materials applications. Systems, where site-specificity of all elementary reactions allows a mapping onto a lattice of discrete active sites, can be addressed within the particularly efficient lattice kMC approach. To this end we describe the versatile kmos software package, which offers a most user-friendly implementation, execution, and evaluation of lattice kMC models of arbitrary complexity in one- to three-dimensional lattice systems, involving multiple active sites in periodic or aperiodic arrangements, as well as site-resolved pairwise and higher-order lateral interactions. Conceptually, kmos achieves a maximum runtime performance which is essentially independent of lattice size by generating code for the efficiency-determining local update of available events that is optimized for a defined kMC model. For this model definition and the control of all runtime and evaluation aspects kmos offers a high-level application programming interface. Usage proceeds interactively, via scripts, or a graphical user interface, which visualizes the model geometry, the lattice occupations and rates of selected elementary reactions, while allowing on-the-fly changes of simulation parameters. We demonstrate the performance and scaling of kmos with the application to kMC models for surface catalytic processes, where for given operation,conditions (temperature and partial pressures of all reactants) central simulation outcomes are catalytic activity and selectivities, surface composition, and mechanistic insight into the occurrence of individual elementary processes in the reaction network.
引用
收藏
页码:2138 / 2150
页数:13
相关论文
共 50 条
[21]   Coupled Coarse Graining and Markov Chain Monte Carlo for Lattice Systems [J].
Kalligiannaki, Evangelia ;
Katsoulakis, Markos A. ;
Plechac, Petr .
NUMERICAL ANALYSIS OF MULTISCALE COMPUTATIONS, 2012, 82 :235-+
[22]   Kinetic Activation-Relaxation Technique and Self-Evolving Atomistic Kinetic Monte Carlo: Comparison of on-the-fly Kinetic Monte Carlo algorithms [J].
Beland, Laurent Karim ;
Osetsky, Yuri N. ;
Stoller, Roger E. ;
Xu, Haixuan .
COMPUTATIONAL MATERIALS SCIENCE, 2015, 100 :124-134
[23]   The effect of introducing stochasticity to kinetic mean-field calculations: Comparison with lattice kinetic Monte Carlo in case of regular solid solutions [J].
Zaporozhets, Tetyana, V ;
Taranovskyy, Andriy ;
Jager, Gabriella ;
Gusak, Andriy M. ;
Erdelyi, Zoltan ;
Toman, Janos J. .
COMPUTATIONAL MATERIALS SCIENCE, 2020, 171
[24]   A hybrid off-lattice kinetic Monte Carlo/molecular dynamics method for amorphous thin film growth [J].
Ntioudis, Stavros ;
Ewen, James P. ;
Dini, Daniele ;
Turner, C. Heath .
COMPUTATIONAL MATERIALS SCIENCE, 2023, 229
[25]   Kinetic lattice Monte Carlo model for oxygen vacancy diffusion in praseodymium doped ceria: Applications to materials design [J].
Dholabhai, Pratik P. ;
Anwar, Shahriar ;
Adams, James B. ;
Crozier, Peter ;
Sharma, Renu .
JOURNAL OF SOLID STATE CHEMISTRY, 2011, 184 (04) :811-817
[26]   A kinetic lattice Monte Carlo study of post-irradiation annealing of model reactor pressure vessel steels [J].
Shu, Shipeng ;
Wells, Peter B. ;
Odette, G. Robert ;
Morgan, Dane .
JOURNAL OF NUCLEAR MATERIALS, 2019, 524 :312-322
[27]   Magnetic properties of checkerboard lattice: a Monte Carlo study [J].
Jabar, A. ;
Masrour, R. ;
Hamedoun, M. ;
Benyoussef, A. .
INDIAN JOURNAL OF PHYSICS, 2017, 91 (12) :1553-1560
[28]   Path integral Monte Carlo on a lattice: Extended states [J].
O'Callaghan, Mark ;
Miller, Bruce N. .
PHYSICAL REVIEW E, 2014, 89 (04)
[29]   Multilevel Monte Carlo algorithm for quantum mechanics on a lattice [J].
Jansen, Karl ;
Muller, Eike H. ;
Scheichl, Robert .
PHYSICAL REVIEW D, 2020, 102 (11)
[30]   Kinetic Monte Carlo Approach to the Effects of Additives in Electrodeposition [J].
Kaneko, Y. ;
Hiwatari, Y. ;
Ohara, K. ;
Asa, F. .
COMPUTATIONAL ELECTROCHEMISTRY, 2011, 35 (27) :7-12