Spin-diffusions and diffusive molecular dynamics

Brittan Farmer, Mitchell Luskin, Petr Plecháč, Gideon Simpson

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Metastable configurations in condensed matter typically fluctuate about local energy minima at the femtosecond time scale before transitioning between local minima after nanoseconds or microseconds. This vast scale separation limits the applicability of classical molecular dynamics (MD) methods and has spurned the development of a host of approximate algorithms. One recently proposed method is diffusive MD which aims at integrating a system of ordinary differential equations describing the likelihood of occupancy by one of two species, in the case of a binary alloy, while quasistatically evolving the locations of the atoms. While diffusive MD has shown itself to be efficient and provide agreement with observations, it is fundamentally a model, with unclear connections to classical MD. In this work, we formulate a spin-diffusion stochastic process and show how it can be connected to diffusive MD. The spin-diffusion model couples a classical overdamped Langevin equation to a kinetic Monte Carlo model for exchange amongst the species of a binary alloy. Under suitable assumptions and approximations, spin-diffusion can be shown to lead to diffusive MD type models. The key assumptions and approximations include a well-defined time scale separation, a choice of spin-exchange rates, a low temperature approximation, and a mean field type approximation. We derive several models from different assumptions and show their relationship to diffusive MD. Differences and similarities amongst the models are explored in a simple test problem.

Original languageEnglish (US)
Article number084003
JournalModelling and Simulation in Materials Science and Engineering
Volume25
Issue number8
DOIs
StatePublished - Oct 24 2017

Keywords

  • diffusive molecular dynamics
  • kinetic Monte Carlo
  • mean field approximation
  • metastability
  • quasistationary distributions

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