Leveraging Thermochemistry Data to Build Accurate Microkinetic Models
Leveraging Thermochemistry Data to Build Accurate Microkinetic Models
复制标题
利用热化学数据构建准确的微动力学模型
DOI：
10.1021/acs.jpcc.0c00491
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Rangarajan, Srinivas
中科院分区：
文献类型：
--
作者：
Tian, Huijie;Rangarajan, Srinivas
使用密度泛函理论（DFT）能量参数化的从头算微动力学模型是一种常用的工具，用于定量反应速率和分析反应机理的先验在多相催化。然而，这样的模型，往往有很大的预测误差，即使它们包括合理的反应步骤和正确的模型的活性位点，这部分是由于所选择的DFT功能的固有的不准确性。借用贝叶斯校准理论的概念，我们表明，可转移的数据驱动的修正DFT能量的形式的高斯过程模型训练单晶吸附量热法数据可以大大提高微动力学模型的准确性。具体地说，我们证明，这种修正提高了3个数量级的单晶Cu（111）表面上的水煤气变换反应的微观动力学模型的预测精度。我们最后表明，高斯过程校正作为先验知识的贝叶斯实验设计框架，学习一个准确的后验微观动力学模型，从几个动力学实验。我们认为，这些结果表明，即使注入小的，相关的，高保真的热化学数据，当可用时，可以系统地和大幅提高微动力学模型的预测精度。
Ab initio microkinetic modeling, parameterized using density functional theory (DFT) energies, is a common tool to quantify reaction rates and analyze reaction mechanisms a priori in heterogeneous catalysis. Such models, however, often have large prediction errors even if they include plausible reaction steps and correctly model the active sites; this is partially due to the intrinsic inaccuracies of the chosen DFT functional. Borrowing concepts from Bayesian calibration theory, we show that transferable data-driven corrections to DFT energies in the form of Gaussian process models trained on single-crystal adsorption calorimetry data can improve the accuracy of microkinetic models substantially. Specifically, we demonstrate that such corrections improve the predictive accuracy of the microkinetic model of the water-gas shift reaction on single-crystal Cu(111) surface by 3 orders of magnitude. We finally show that Gaussian process corrections serve as informed priors in a Bayesian experimental design framework to learn an accurate a posteriori microkinetic model from few kinetic experiments. We posit that these results suggest that even infusing small, related, high-fidelity thermochemistry data, when available, can systematically and substantially improve the predictive accuracy of microkinetic models.
影响因子：
12.9
作者：
Grabow, L. C.;Mavrikakis, M.
通讯作者：
Mavrikakis, M.