4.4 Article

Monte Carlo study of Cu precipitation in bcc-Fe: temperature-dependent cluster expansion versus local chemical environment potentials

Publisher

IOP Publishing Ltd
DOI: 10.1088/1361-651X/abe5b2

Keywords

Monte Carlo simulation; cluster expansion; kinetic Monte Carlo; Fe– Cu alloys

Funding

  1. Austrian Science Fund (FWF) within the SFB ViCoM (FWF-Spezialforschungsbereich) [F41]
  2. COMET program within the K2 Center 'Integrated ComputationalMaterial, Process and Product Engineering (IC-MPPE)' [859480]
  3. Austrian Federal Ministries for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK)
  4. Austrian research funding association (FFG)

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The phase decomposition in binary Fe1-xCux system was studied using Monte Carlo simulations. The research combined density functional theory calculations and local chemical environment approach to predict and analyze the evolution of precipitates.
Phase decomposition in binary Fe1-xCux is studied using Monte Carlo simulations. Initially, density functional theory calculations are utilized to determine reference energies of various Fe-Cu compounds that serve as input for a temperature and composition-dependent cluster expansion. On this basis, the thermodynamic properties of the bcc Fe-Cu system are predicted and used to simulate the equilibrium constitution of bcc Cu-rich precipitates in an Fe-rich solid solution at various temperatures and supersaturations. Complementarily, computationally efficient pair potentials are developed in the local chemical environment approach that are calibrated on the first principles-cluster expansion results. These are then utilized in large-scale simulations for analysis of the multi-particle precipitate evolution. It is concluded that both approaches provide comparable information in terms of the precipitate radius as well as interface constitution. Whereas the cluster expansion ('full-information') path is especially useful in predicting energies of various ground state configurations for small systems, the local chemical environment approach ('fast-computation') path is particularly useful in evaluation of cluster formation kinetics and evolution statistics.

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