The enhanced extended phenomenological kinetics method to deal with timescale disparity problem among different reaction pathways,Journal of Computational Chemistry

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The enhanced extended phenomenological kinetics method to deal with timescale disparity problem among different reaction pathways
Journal of Computational Chemistry ( IF 3.4 ) Pub Date : 2020-07-02 , DOI: 10.1002/jcc.26374
Chen Ding ₁ , Jingwei Weng ₁ , Tonghao Shen ₁ , Xin Xu ₁

Affiliation

Kinetic Monte Carlo method can provide valuable mechanistic insights for catalytic systems. Nonetheless, it suffers from the notorious problem of timescale disparity due to the existence of the complex catalytic network that consists of fast events and slow events. Previously, we have proposed the extended phenomenological kinetics (XPK) method that effectively deals with the timescale disparity problem between diffusion and reaction. However, it remains a great challenge to simulate systems with timescale disparity among different reaction pathways, which is important when selectivity is the major concern. In this study, we implement the enhanced XPK method to address this problem. The new algorithm works by identifying states connected through fast transitions and compressing them into a “superstate” when the chosen states satisfy a local steadystate condition. This state compression algorithm simplifies the reaction network by concealing the fast transitions. The accuracy and efficiency of the algorithm are demonstrated by two model systems: selective catalytic hydrogenation and selective catalytic decomposition. The enhanced XPK method is expected to be beneficial to the kinetic simulations of catalytic systems, especially those with complex reaction networks.

中文翻译：

处理不同反应途径时间尺度差异问题的增强扩展唯象动力学方法

动力学蒙特卡罗方法可以为催化系统提供有价值的机械见解。尽管如此，由于存在由快速事件和慢速事件组成的复杂催化网络，它遭受了臭名昭著的时间尺度差异问题。之前，我们提出了扩展的现象动力学（XPK）方法，该方法有效地处理了扩散和反应之间的时间尺度差异问题。然而，在不同的反应途径中模拟具有时间尺度差距的系统仍然存在巨大挑战，这在选择性是主要关注点时是重要的。在这项研究中，我们实施了增强型 XPK 方法来解决这个问题。新算法的工作原理是识别通过快速转换连接的状态，并在所选状态满足局部稳态条件时将它们压缩为“超状态”。这种状态压缩算法通过隐藏快速转换来简化反应网络。该算法的准确性和效率由两个模型系统证明：选择性催化加氢和选择性催化分解。增强的 XPK 方法有望有利于催化系统的动力学模拟，尤其是那些具有复杂反应网络的系统。选择性催化加氢和选择性催化分解。增强的 XPK 方法有望有利于催化系统的动力学模拟，尤其是那些具有复杂反应网络的系统。选择性催化加氢和选择性催化分解。增强的 XPK 方法有望有利于催化系统的动力学模拟，尤其是那些具有复杂反应网络的系统。

更新日期：2020-07-02

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