Stochastic kinetic mean field model
| Autor: | Bence Gajdics, M. O. Pasichnyy, János J. Tomán, Zoltán Erdélyi, V. M. Bezpalchuk, Andriy Gusak |
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| Rok vydání: | 2016 |
| Předmět: |
010302 applied physics
Stochastic process General Physics and Astronomy Fizikai tudományok 02 engineering and technology 021001 nanoscience & nanotechnology Kinetic energy 01 natural sciences Amplitude Természettudományok Mean field theory Hardware and Architecture Lattice (order) 0103 physical sciences Kinetic Monte Carlo Statistical physics 0210 nano-technology Brownian motion Mathematics Potts model |
| Zdroj: | Computer Physics Communications. 204:31-37 |
| ISSN: | 0010-4655 |
| Popis: | This paper introduces a new model for calculating the change in time of three-dimensional atomic configurations. The model is based on the kinetic mean field (KMF) approach, however we have transformed that model into a stochastic approach by introducing dynamic Langevin noise. The result is a stochastic kinetic mean field model (SKMF) which produces results similar to the lattice kinetic Monte Carlo (KMC). SKMF is, however, far more cost-effective and easier to implement the algorithm (open source program code is provided on http://skmf.eu website). We will show that the result of one SKMF run may correspond to the average of several KMC runs. The number of KMC runs is inversely proportional to the amplitude square of the noise in SKMF. This makes SKMF an ideal tool also for statistical purposes. |
| Databáze: | OpenAIRE |
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