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Transport in Disordered Media
Published in Juan Bisquert, The Physics of Solar Energy Conversion, 2020
The kinetic Monte Carlo (KMC) simulation is a stochastic computational procedure that allows for a flexible description of transport of charge carriers in a network of traps without huge computational demands (Anta et al., 2008). In the KMC calculation, a certain number of carriers are allowed to jump between neighboring traps. The hopping time between two traps labeled i and j is tij=−ln(R)(vij)−1,
Transport in Disordered Media
Published in Juan Bisquert, Nanostructured Energy Devices, 2017
The kinetic Monte-Carlo (KMC) simulation is a stochastic computational procedure that allows for a flexible description of transport of charge carriers in a network of traps without huge computational demands (Anta et al., 2008). In the KMC calculation, a certain number of carriers are allowed to jump between neighboring traps. The hopping time between two traps labeled i and j is tij=-ln(R)(vij)-1, $$ t_{{ij}} = - ln(R)(v_{{ij}} )^{{ - 1}} , $$
Grizzly and BlackBear: Structural Component Aging Simulation Codes
Published in Nuclear Technology, 2021
Benjamin W. Spencer, William M. Hoffman, Sudipta Biswas, Wen Jiang, Alain Giorla, Marie A. Backman
Some of these microstructure evolution models are based on continuum theories, while many of them, particularly those that represent behavior at the atomistic scale, are not. Based on continuous finite element theory, Grizzly is applicable to modeling techniques that represent continuous behavior, such as phase field, crystal plasticity, and continuum mechanics. It also can be used to solve the ordinary differential equations (ODEs) of mean-field cluster dynamics modeling. It does not, however, support techniques such as molecular dynamics and kinetic Monte Carlo that are used at atomistic scales; other codes are used for those purposes. Grizzly does provide support for reading in solutions from some of these atomistic-scale models and mapping them to continuous solution fields, which has been used for modeling precipitate nucleation in a kinetic Monte Carlo code, and then using phase-field models to predict their coarsening and growth.17
Modelling of short-range ordering kinetics in dilute multicomponent substitutional solid solutions
Published in Philosophical Magazine, 2020
J. Svoboda, D. Holec, M. Popov, G.A. Zickler, F.D. Fischer
The motivation of this paper is to present a general atomistic-statistical-thermodynamic model for the treatment of kinetics of numbers of Cs and Ps in a dilute multicomponent system under varying temperature in a compact way. In contrast to previous statistical mechanics models, the calculation of Gibbs energy of the system is based on the division of the system into subsystems and application of the established Bragg–Williams approximation to these individual subsystems. The state of the system is then described by concentrations of atoms in Cs and Ps considered as independent internal state variables. The bonding energies are calculated by ab initio methods in a standard way. To determine the kinetics of the system, additionally, only tracer diffusion coefficients of all solute components are needed. Therefore, the model combines ab initio calculations with non-equilibrium thermodynamics and can be utilised for an improved determination of the thermodynamic properties of solid solutions accounting also for their thermal history. It is also necessary to be noted that the present kinetic model allows calculations within well acceptable computation times, which cannot be achieved using other kinetic/atomistic methods. To check the accuracy of the present model the results of simulations are compared with simulations by kinetic Monte Carlo Method with a very good agreement.
First-principles prediction of high oxygen-ion conductivity in trilanthanide gallates Ln3GaO6
Published in Science and Technology of Advanced Materials, 2019
Joohwi Lee, Nobuko Ohba, Ryoji Asahi
The lowest Em value among the five migration paths is found to be path II-1, which is between O(II)–O(II) (see Supplementary Figure S5). In addition, path II-1 is delocalized and connected to the neighboring cell; therefore, this can be the dominant migration path. In contrast, paths II-4, II-5, and III-1 with higher (Em+ΔEref) than the Em value of path II-1 are localized and not connected; therefore, complex combinations of paths for migrating VO2+ to reach the neighboring cell are required. Path III-2 is delocalized and connected to the neighboring cell; however, this path is formed between the less stable O(III) sites. To observe total ionic movements on various migration paths with time scale, other methods such as kinetic Monte Carlo [58,59] and molecular dynamics are necessary. We used FPMD in this study; thus, we will discuss the DO that is obtained from the total ionic movements by FPMD in the next subsection.