Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, United Kingdom.
J Chem Phys. 2017 Jul 14;147(2):024105. doi: 10.1063/1.4991690.
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.
表面催化反应动力学的建模对于反应器和化学反应过程的设计至关重要。大多数微观动力学模型采用平均场近似，通过假设空间上不相关的吸附物来对催化动力学进行近似描述。另一方面，动力学蒙特卡罗 (KMC) 方法提供了一种离散空间连续时间的随机表述，能够在吸附层中准确处理空间相关性，但计算成本很高。在这项工作中，我们使用所谓的簇平均场方法来开发更高阶的近似，通过在更高的细节水平上处理空间相关性，系统地提高动力学模型的准确性。我们进一步将我们的方法应用于包含第一近邻横向相互作用的简化 NO 氧化模型，并构建了一系列精度不断提高的近似，将其与 KMC 和平均场进行了比较。结果发现，后者的表现相当差，对该体系的周转率高估了几个数量级。另一方面，我们的近似方法虽然比传统的平均场处理计算强度更大，但与 KMC 模拟相比仍然可以实现巨大的计算节省，从而为在多尺度建模框架中使用它们开辟了道路。
