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Elastodiffusion and cluster mobilities using kinetic Monte Carlo simulations: fast first-passage algorithms for reversible diffusion processes

Abstract : The microstructural evolution of metals and alloys is governed by the diffusion of defects over complex energy landscapes. Whenever metastability occurs in atomistic simulations, well-separated time scales emerge making it necessary to implement event-based kinetic models at larger scales. The crucial task then involves characterizing the important events contributing to mass transport. We herein describe fast first-passage algorithms based on the theory of absorbing Markov chains assuming that defects undergo reversible diffusion. We show that the absorbing transition rate matrix can be transformed into a symmetric definite-positive matrix enabling us to implement direct and iterative sparse solvers. The efficiency of the approach is demonstrated with direct computations of elastodiffusion properties around a cavity in Aluminum and Monte Carlo computations of cluster diffusivity in low alloyed Manganese steels.
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Submitted on : Friday, January 17, 2020 - 12:00:15 PM
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Manuel Athenes, Savneet Kaur, Gilles Adjanor, Thomas Vanacker, Thomas Jourdan. Elastodiffusion and cluster mobilities using kinetic Monte Carlo simulations: fast first-passage algorithms for reversible diffusion processes. Physical Review Materials, American Physical Society, 2019, 3 (10), pp.103802. ⟨10.1103/PhysRevMaterials.3.103802⟩. ⟨cea-02443620⟩

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