Mean field theories for the description of diffusion and phase transformations controlled by diffusion

文献信息

发布日期 2004-06-15
DOI 10.1039/B401884B
影响因子 3.676
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摘要

A self-consistent mean field (SCMF) theory starting from an atomic diffusion model can predict most of the correlation effects induced by the vacancy diffusion mechanism while being coherent with thermodynamics. For that purpose a time dependent effective Hamiltonian is introduced into the non-equilibrium distribution function. Such effective Hamiltonian makes a new hierarchy of approximations to appear in parallel to the thermodynamic mean-field approximations. We explain how each level of approximation is related to correlation effects and how kinetics of phase transformation results from both thermodynamics and diffusion properties. A first example is the radiation induced segregation (RIS) at grain boundary (GB) in austenitic steels where thermodynamics is shown to play an important role under irradiation. In a second example, we demonstrate how mean-field approximations combined with mesoscopic theories like the classical nucleation theory can describe the kinetics of precipitation, leading to a prediction of the cluster size distributions in the solid solution and of the steady state nucleation rates in agreement with kinetic Monte Carlo simulations.

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来源期刊

Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
自引率: 10.3%
年发文量: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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