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).

中文翻译：

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使用并行处理和速率常数重缩放的晶格动力学蒙特卡洛模拟的加速度和灵敏度分析

动力学蒙特卡洛（KMC）模拟提供了对通过实验或均场微动力学模型无法获得的催化反应的见解。KMC模型的敏感性分析评估了预测对参数扰动的鲁棒性，并确定了化学反应网络中确定速率的步骤。化学反应网络中的刚性是一个普遍存在的特征，要求KMC模型运行时间长，并且使得基于似然比方法的有效灵敏度分析无法使用。通过采用两种加速技术：速率常数重缩放和并行处理，我们解决了在未知时标的系统中有效地进行KMC仿真和执行准确的灵敏度分析的挑战。我们开发统计标准，以确保对非平衡稳态条件进行足够的采样。我们的方法具有加速仿真本身和启用似然比灵敏度分析的双重好处，与有限差分灵敏度分析相比，它可以进一步提高速度。结果，似然比方法可以应用于真实化学。我们将我们的方法学应用于Pt（111）上的水煤气变换反应。