Beyond mean-field approximations for accurate and computationally efficient models of on-lattice chemical kinetics.
Beyond mean-field approximations for accurate and computationally efficient models of on-lattice chemical kinetics.
复制标题
超越平均场近似，实现精确且计算高效的晶格化学动力学模型。
DOI：
10.1063/1.4991690
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
M. Stamatakis
中科院分区：
文献类型：
--
作者：
Miguel Pineda;M. Stamatakis
表面催化反应动力学模型对于反应器和化学过程的设计是必不可少的。大多数的微观动力学模型采用平均场近似，这导致通过假设空间不相关的吸附物的催化动力学的近似描述。另一方面，动力学蒙特卡罗（KMC）方法提供了一个离散空间连续时间的随机制定，使空间相关性的准确治疗的adlayer，但在一个显着的计算成本。在这项工作中，我们使用所谓的集群平均场方法来开发高阶近似，系统地提高动力学模型的准确性，通过处理空间相关性在一个渐进的更高层次的细节。我们进一步证明了我们的方法上减少模型NO氧化纳入第一最近邻横向相互作用，并构建了一个序列的近似越来越高的精度，我们比较KMC和平均场。后者被发现表现相当差，高估了几个数量级的周转频率为这个系统。另一方面，我们的近似，而更密集的计算比传统的平均场处理，仍然实现了巨大的计算节省相比，KMC模拟，从而开辟了在多尺度建模框架中使用它们的方式。
Modeling the kinetics of surface catalyzed reactions is essential for the design of reactors and chemical processes. The majority of microkinetic models employ mean-field approximations, which lead to an approximate description of catalytic kinetics by assuming spatially uncorrelated adsorbates. On the other hand, kinetic Monte Carlo (KMC) methods provide a discrete-space continuous-time stochastic formulation that enables an accurate treatment of spatial correlations in the adlayer, but at a significant computation cost. In this work, we use the so-called cluster mean-field approach to develop higher order approximations that systematically increase the accuracy of kinetic models by treating spatial correlations at a progressively higher level of detail. We further demonstrate our approach on a reduced model for NO oxidation incorporating first nearest-neighbor lateral interactions and construct a sequence of approximations of increasingly higher accuracy, which we compare with KMC and mean-field. The latter is found to perform rather poorly, overestimating the turnover frequency by several orders of magnitude for this system. On the other hand, our approximations, while more computationally intense than the traditional mean-field treatment, still achieve tremendous computational savings compared to KMC simulations, thereby opening the way for employing them in multiscale modeling frameworks.
影响因子：
6.3
作者：
Hoffmann, Max J.;Matera, Sebastian;Reuter, Karsten
通讯作者：
Reuter, Karsten