The effect of introducing stochasticity to kinetic mean-field calculations: Comparison with lattice kinetic Monte Carlo in case of regular solid solutions

Tetyana V. Zaporozhets, Andriy Taranovskyy, Gabriella Jáger, Andriy M. Gusak, Zoltán Erdélyi, János J. Tomán

Research output: Contribution to journalArticle


In present work we discuss the problem of introducing stochasticity into 3D atomistic kinetic mean-field simulations. As this is a new approach for simulating the time evolution of material systems, it should be positioned in the field of available methods. Compared to the most used stochastic techniques: while atomistic kinetic Monte Carlo (KMC) methods generate microstates of the system they simulate, stochastic kinetic mean-field (SKMF) seems to generate mesostates of the system during a finite time-window. Previously, strong interrelation have been found for the dispersion of composition fluctuations in ideal solutions between SKMF states and averaged KMC states. In present work we compare the statistical nature of fluctuations in the two approaches in case of equilibrium solid solutions with non-zero mixing energies. At the investigated high temperature cases we show how at a certain noise amplitude in SKMF the composition fluctuations can be related to results received by averaging a certain number of independent KMC states. Furthermore, the correlation between the neighboring sites, emerging because of the chemical interactions, shows the same statistical behavior in both methods as well.

Original languageEnglish
Article number109251
JournalComputational Materials Science
Publication statusPublished - Jan 2020



  • Fluctuation phenomena
  • Mean-field
  • Monte Carlo
  • Noise
  • Regular solution
  • Statistical mechanics of model systems
  • Thermodynamics of solutions

ASJC Scopus subject areas

  • Computer Science(all)
  • Chemistry(all)
  • Materials Science(all)
  • Mechanics of Materials
  • Physics and Astronomy(all)
  • Computational Mathematics

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