Elastodiffusion and cluster mobilities using kinetic Monte Carlo simulations: Fast first-passage algorithms for reversible diffusion processes
Elastodiffusion and cluster mobilities using kinetic Monte Carlo simulations: Fast first-passage algorithms for reversible diffusion processes
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DOI：
10.1103/physrevmaterials.3.103802
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发表时间：
2019-10-08
影响因子：
3.4
通讯作者：
Jourdan, Thomas
中科院分区：
文献类型：
--
作者：
Athenes, Manuel;Kaur, Savneet;Jourdan, Thomas
金属和合金的微观结构演化受缺陷在复杂能量环境中的扩散控制。每当亚稳态发生在原子模拟，以及分离的时间尺度出现，使其有必要在更大的尺度上实现基于事件的动力学模型。然后，关键的任务涉及描述有助于大规模运输的重要事件。我们在这里描述快速的第一次通过算法的基础上吸收马尔可夫链的理论，假设缺陷进行可逆扩散。我们发现，吸收转移率矩阵可以转化为一个对称的正定矩阵，使我们能够实现直接和迭代稀疏求解器。通过直接计算铝中孔洞周围的弹性扩散性质和Monte Carlo计算低合金锰钢中的团簇扩散系数，证明了该方法的有效性。
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 timescales 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 andMonte Carlo computations of cluster diffusivity in low-alloyed manganese steels.