Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, USA.
J Chem Phys. 2017 Oct 28;147(16):164103. doi: 10.1063/1.4998926.
Kinetic Monte Carlo (KMC) simulation provides insights into catalytic reactions unobtainable with either experiments or mean-field microkinetic models. Sensitivity analysis of KMC models assesses the robustness of the predictions to parametric perturbations and identifies rate determining steps in a chemical reaction network. Stiffness in the chemical reaction network, a ubiquitous feature, demands lengthy run times for KMC models and renders efficient sensitivity analysis based on the likelihood ratio method unusable. We address the challenge of efficiently conducting KMC simulations and performing accurate sensitivity analysis in systems with unknown time scales by employing two acceleration techniques: rate constant rescaling and parallel processing. We develop statistical criteria that ensure sufficient sampling of non-equilibrium steady state conditions. Our approach provides the twofold benefit of accelerating the simulation itself and enabling likelihood ratio sensitivity analysis, which provides further speedup relative to finite difference sensitivity analysis. As a result, the likelihood ratio method can be applied to real chemistry. We apply our methodology to the water-gas shift reaction on Pt(111).
动力学蒙特卡罗 (KMC) 模拟为催化反应提供了实验或平均场微动力学模型无法获得的见解。KMC 模型的敏感性分析评估了预测对参数摄动的稳健性，并确定化学反应网络中的速率决定步骤。化学反应网络的刚性是普遍存在的特征，这要求 KMC 模型具有较长的运行时间，并且使得基于似然比方法的有效敏感性分析无法使用。我们通过采用两种加速技术来解决在具有未知时间尺度的系统中有效进行 KMC 模拟和执行准确敏感性分析的挑战：速率常数缩放和平行处理。我们开发了统计标准，以确保非平衡稳态条件的充分采样。我们的方法提供了双重好处，即加速模拟本身并能够进行似然比敏感性分析，这相对于有限差分敏感性分析提供了进一步的加速。因此，可以将似然比方法应用于实际化学。我们将我们的方法应用于 Pt(111)上的水气变换反应。
