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  • 1. Gutic, Sanjin J.
    et al.
    Dobrota, Ana S.
    Leetmaa, Mikael
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering.
    Skorodumova, Natalia V.
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering. Department of Physics and Astronomy, Uppsala University, Box 516, 751 20 Uppsala, Sweden.
    Mentus, Slavko V.
    Pasti, Igor A.
    Improved catalysts for hydrogen evolution reaction in alkaline solutions through the electrochemical formation of nickel-reduced graphene oxide interface2017In: Physical Chemistry, Chemical Physics - PCCP, ISSN 1463-9076, E-ISSN 1463-9084, Vol. 19, no 20, p. 13281-13293Article in journal (Refereed)
    Abstract [en]

    H-2 production via water electrolysis plays an important role in hydrogen economy. Hence, novel cheap electrocatalysts for the hydrogen evolution reaction ( HER) are constantly needed. Here, we describe a simple method for the preparation of composite catalysts for H-2 evolution, consisting in simultaneous reduction of the graphene oxide film, and electrochemical deposition of Ni on its surface. The obtained composites (Ni@rGO), compared to pure electrodeposited Ni, show an improved electrocatalytic activity towards HER in alkaline media. We found that the activity of the Ni@rGO catalysts depends on the surface composition ( Ni vs. C mole ratio) and on the level of structural disorder of the rGO support. We suggest that HER activity is improved via H-ads spillover from the Ni particles to the rGO support, where quick recombination to molecular hydrogen is favored. A deeper insight into such a mechanism of H-2 production was achieved by kinetic Monte-Carlo simulations. These simulations enabled the reproduction of experimentally observed trends under the assumption that the support can act as a Hads acceptor. We expect that the proposed procedure for the production of novel HER catalysts could be generalized and lead to the development of a new generation of HER catalysts by tailoring the catalyst/support interface.

  • 2.
    Leetmaa, Mikael
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Multiscale Materials Modelling.
    Skorodumova, Natalia
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Multiscale Materials Modelling.
    Kinetic Monte Carlo modelling of ion diffusion. Example: Ceria2013In: EFC 2013 - Proceedings of the 5th European Fuel Cell Piero Lunghi Conference, 2013, p. 53-54Conference paper (Refereed)
    Abstract [en]

    Development of theoretical tools allowing us to study diffusion in solids at different scales is important for rational materials design. One of the effective approaches is a combination of Kinetic Monte Carlo technique with first principle electronic structure calculations. The KMClib program developed by us is a robust and flexible tool for studying diffusion, in particular, in ionconducting materials. The code has unique features such as on-thefly custom rate calculations, simulation of electrical bias and possibilities for on-the-fly analysis. It can be used in conjunction with ab initio calculations or just providing it with rates estimated from experiment or obtained in any other way. As an example of the code performance we present a simulation of oxygen diffusion in ceria.

  • 3.
    Leetmaa, Mikael
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Multiscale Materials Modelling.
    Skorodumova, Natalia V.
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Multiscale Materials Modelling. Department of Physics and Astronomy, Uppsala University.
    KMCLib: A general framework for lattice kinetic Monte Carlo (KMC) simulations2014In: Computer Physics Communications, ISSN 0010-4655, E-ISSN 1879-2944, Vol. 185, no 9, p. 2340-2349Article in journal (Refereed)
    Abstract [en]

    KMCLib is a general framework for lattice kinetic Monte Carlo (KMC) simulations. The program can handle simulations of the diffusion and reaction of millions of particles in one, two, or three dimensions, and is designed to be easily extended and customized by the user to allow for the development of complex custom KMC models for specific systems without having to modify the core functionality of the program. Analysis modules and on-the-fly elementary step diffusion rate calculations can be implemented as plugins following a well-defined API. The plugin modules are loosely coupled to the core KMCLib program via the Python scripting language. KMCLib is written as a Python module with a backend C++ library. After initial compilation of the backend library KMCLib is used as a Python module; input to the program is given as a Python script executed using a standard Python interpreter. We give a detailed description of the features and implementation of the code and demonstrate its scaling behavior and parallel performance with a simple one-dimensional A-B-C lattice KMC model and a more complex three-dimensional lattice KMC model of oxygen-vacancy diffusion in a fluorite structured metal oxide. KMCLib can keep track of individual particle movements and includes tools for mean square displacement analysis, and is therefore particularly well suited for studying diffusion processes at surfaces and in solids. Program summary Program title: KMCLib Catalogue identifier: AESZ_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AESZ_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU General Public License, version 3 No. of lines in distributed program, including test data, etc.: 49 064 No. of bytes in distributed program, including test data, etc.: 1 575 172 Distribution format: tar.gz Programming language: Python and C++. Computer: Any computer that can run a C++ compiler and a Python interpreter. Operating system: Tested on Ubuntu 12.4 LTS, CentOS release 5.9, Mac OSX 10.5.8 and Mac OSX 10.8.2, but should run on any system that can have a C++ compiler, MPI and a Python interpreter. Has the code been vectorized or parallelized?: Yes. From one to hundreds of processors depending on the type of input and simulation. RAM: From a few megabytes to several gigabytes depending on input parameters and the size of the system to simulate. Classification: 4.13, 16.13. External routines: KMCLib uses an external Mersenne Twister pseudo random number generator that is included in the code. A Python 2.7 interpreter and a standard C++ runtime library are needed to run the serial version of the code. For running the parallel version an MPI implementation is needed, such as e.g. MPICH from http://www.mpich.org or Open-MPI from http://www.open-mpi.org. SWIG (obtainable from http://www.swig.org/) and CMake (obtainable from http://www.cmake.org/) are needed for building the backend module, Sphinx (obtainable from http://sphinx-doc.org) for building the documentation and CPPUNIT (obtainable from http://sourceforge.net/projects/cppunit/) for building the C++ unit tests. Nature of problem: Atomic scale simulation of slowly evolving dynamics is a great challenge in many areas of computational materials science and catalysis. When the rare-events dynamics of interest is orders of magnitude slower than the typical atomic vibrational frequencies a straight-forward propagation of the equations of motions for the particles in the simulation cannot reach time scales of relevance for modeling the slow dynamics. Solution method: KMCLib provides an implementation of the kinetic Monte Carlo (KMC) method that solves the slow dynamics problem by utilizing the separation of time scales between fast vibrational motion and the slowly evolving rare-events dynamics. Only the latter is treated explicitly and the system is simulated as jumping between fully equilibrated local energy minima on the slow-dynamics potential energy surface. Restrictions: KMCLib implements the lattice KMC method and is as such restricted to geometries that can be expressed on a grid in space. Unusual features: KMCLib has been designed to be easily customized, to allow for user-defined functionality and integration with other codes. The user can define her own on-the-fly rate calculator via a Python API, so that site-specific elementary process rates, or rates depending on long-range interactions or complex geometrical features can easily be included. KMCLib also allows for on-the-fly analysis with user-defined analysis modules. KMCLib can keep track of individual particle movements and includes tools for mean square displacement analysis, and is therefore particularly well suited for studying diffusion processes at surfaces and in solids. Additional comments: The full documentation of the program is distributed with the code and can also be found at http://www.github.com/leetmaa/KMCLib/manual Running time: From a few seconds to several days depending on the type of simulation and input parameters.

  • 4.
    Leetmaa, Mikael
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Multiscale Materials Modelling. Uppsala University, Sweden.
    Skorodumova, Natalia V.
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Multiscale Materials Modelling. Uppsala University, Sweden.
    Mean square displacements with error estimates from non-equidistant time-step kinetic Monte Carlo simulations2015In: Computer Physics Communications, ISSN 0010-4655, E-ISSN 1879-2944, Vol. 191, p. 119-124Article in journal (Refereed)
    Abstract [en]

    We present a method to calculate mean square displacements (MSD) with error estimates from kinetic Monte Carlo (KMC) simulations of diffusion processes with non-equidistant time-steps. An analytical solution for estimating the errors is presented for the special case of one moving particle at fixed rate constant. The method is generalized to an efficient computational algorithm that can handle any number of moving particles or different rates in the simulated system. We show with examples that the proposed method gives the correct statistical error when the MSD curve describes pure Brownian motion and can otherwise be used as an upper bound for the true error.

  • 5.
    Nilsson, Johan O.
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering.
    Leetmaa, Mikael
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering.
    Vekilova, Olga Yu.
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering. Department of Physics and Astronomy, Uppsala University, Box 516, 751 20 Uppsala, Sweden.
    Simak, Sergei I.
    Skorodumova, Natalia V.
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering. Department of Physics and Astronomy, Uppsala University, Box 516, 751 20 Uppsala, Sweden.
    Oxygen diffusion in ceria doped with rare-earth elements2017In: Physical Chemistry, Chemical Physics - PCCP, ISSN 1463-9076, E-ISSN 1463-9084, Vol. 21, p. 13723-13730Article in journal (Refereed)
    Abstract [en]

    We examine the effects of the dopant type and the dopant distribution on the ion diffusion in ceria doped with rare-earth elements (Pr, Nd, Pm, Sm, Eu, and Gd). Diffusion is simulated by means of a Kinetic Monte Carlo method using input transition rates derived from diffusion barriers calculated in the framework of density functional theory (DFT). Based on diffusion simulations, we discuss the characteristics of the dopants in terms of the diffusion barriers, and study oxygen ion trajectories for different dopants and distributions. Our simulations show a trend of increasing ion diffusivity with increasing atomic number for all distributions.

  • 6.
    Nilsson, Johan O.
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering.
    Leetmaa, Mikael
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering.
    Wang, B.
    Zguns, Pjotrs
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering. Uppsala University, Sweden.
    Pašti, I.
    Sandell, A.
    Skorodumova, Natalia
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering. Uppsala University, Sweden.
    Modeling Kinetics of Water Adsorption on the Rutile TiO2 (110) Surface: Influence of Exchange-Correlation Functional2018In: Physica status solidi. B, Basic research, ISSN 0370-1972, E-ISSN 1521-3951, Vol. 255, no 3, article id 1700344Article in journal (Refereed)
    Abstract [en]

    The accuracy of the theoretical description of materials properties in the framework of density functional theory (DFT) inherently depends on the exchange-correlation (XC) functional used in the calculations. Here we investigate the influence of the choice of a XC functional (PBE, RPBE, PW91, and PBE0) on the kinetics of the adsorption, diffusion and dissociation of water on the rutile TiO2(110) surface using a combined Kinetic Monte Carlo (KMC) – DFT approach, where the KMC simulations are based on the barriers for the aforementioned processes calculated with DFT. We also test how the adsorption energy of intact and dissociated water molecules changes when dispersion interactions are included into the calculations. We consider the beginning of the water layer formation varying coverage up to 0.2 monolayer (ML) at temperatures up to 180 K. We demonstrate that the dynamics of the simulated water–titania system is extremely sensitive to the choice of the XC functional.

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