Evaluating the benefits of kinetic Monte Carlo and microkinetic modeling for catalyst design studies in the presence of lateral interactions
Evaluating the benefits of kinetic Monte Carlo and microkinetic modeling for catalyst design studies in the presence of lateral interactions
复制标题
评估动力学蒙特卡罗和微动力学建模在存在横向相互作用的情况下对催化剂设计研究的好处
作者：
Xiao Li;L. Grabow
流行的计算催化剂设计策略依赖于反应性描述符的识别，这些描述符可以与Brønsted−Evans−Polanyi（BEP）和标度关系一起用作微动力学模型（MKM）的输入，以预测活性或选择性趋势。这种方法的主要好处是与大材料空间的固有维数降低到几个催化剂描述符有关。相反，有充分的文献证明，一小组描述符不足以捕捉真实的催化系统的错综复杂和复杂性。通过横向吸附物-吸附物相互作用的覆盖效应可以缩小简化的描述符预测和真实的系统之间的差距，但平均场MKM不能正确地解释局部覆盖效应。平均场近似的这一缺点可以通过切换到基于晶格的动力学蒙特卡罗（kMC）方法来纠正，该方法使用吸附质-吸附质横向相互作用的簇展开表示，以典型的CO氧化反应为例，我们严格评估了kMC在趋势预测和计算成本方面的优势，当只使用一小部分输入参数时。在确认在没有横向相互作用的情况下，KMC和MKM方法产生相同的趋势和机理信息后，我们观察到两种动力学模型之间的实质性差异时，引入横向相互作用。平均场的实现直接应用覆盖校正的描述符，造成人为的过度预测的强结合金属的活性。相比之下，kMC实现中的簇扩展可以区分高活性金属，但它对所包含的相互作用参数集非常敏感。考虑到计算筛选依赖于一组最小的描述符，MKM在一个条件下对其进行合理的趋势预测。MKM方法的计算成本比kMC低三个数量级，为计算催化剂设计提供了更好的切入点。
Popular computational catalyst design strategies rely on the identification of reactivity descriptors, which can be used along with Brønsted−Evans−Polanyi (BEP) and scaling relations as input to a microkinetic model (MKM) to make predictions for activity or selectivity trends. The main benefit of this approach is related to the inherent dimensionality reduction of the large material space to just a few catalyst descriptors. Conversely, it is well documented that a small set of descriptors is insufficient to capture the intricacies and complexities of a real catalytic system. The inclusion of coverage effects through lateral adsorbate-adsorbate interactions can narrow the gap between simplified descriptor predictions and real systems, but mean-field MKMs cannot properly account for local coverage effects. This shortcoming of the mean-field approximation can be rectified by switching to a lattice-based kinetic Monte Carlo (kMC) method using cluster expansion representation of adsorbate−adsorbate lateral interactions.Using the prototypical CO oxidation reaction as an example, we critically evaluate the benefits of kMC over MKM in terms of trend predictions and computational cost when using only a small set of input parameters. After confirming that in the absence of lateral interactions the kMC and MKM approaches yield identical trends and mechanistic information, we observed substantial differences between the two kinetic models when lateral interactions were introduced. The mean-field implementation applies coverage corrections directly to the descriptors, causing an artificial overprediction of the activity of strongly binding metals. In contrast, the cluster expansion in kMC implementation can differentiate among the highly active metals but it is very sensitive to the set of included interaction parameters. Considering that computational screening relies on a minimal set of descriptors, for which MKM makes reasonable trend predictions at a ca. three orders of magnitude lower computational cost than kMC, the MKM approach does provide a better entry point for computational catalyst design.