Blais Christopher J, Xu Chao, West Richard H
Department of Chemical Engineering, Northeastern University, Boston, Massachusetts 02115, United States.
J Phys Chem C Nanomater Interfaces. 2024 Oct 3;128(41):17418-17433. doi: 10.1021/acs.jpcc.4c05107. eCollection 2024 Oct 17.
Accurate and complete microkinetic models (MKMs) are powerful for anticipating the behavior of complex chemical systems at different operating conditions. In heterogeneous catalysis, they can be further used for the rapid development and screening of new catalysts. Density functional theory (DFT) is often used to calculate the parameters used in MKMs with relatively high fidelity. However, given the high cost of DFT calculations for adsorbates in heterogeneous catalysis, linear scaling relations (LSRs) and machine learning (ML) models were developed to give rapid estimates of the parameters in MKM. Regardless of the method, few studies have attempted to quantify the uncertainty in catalytic MKMs, as the uncertainties are often orders of magnitude larger than those for gas phase models. This study explores uncertainty quantification and Bayesian Parameter Estimation for thermodynamic parameters calculated by DFT, LSRs, and GemNet-OC, a ML model developed under the Open Catalyst Project. A model for catalytic partial oxidation of methane (CPOX) on Rhodium was chosen as a case study, in which the model's thermodynamic parameters and their associated uncertainties were determined using DFT, LSR, and GemNet-OC. Markov Chain Monte Carlo coupled with Ensemble Slice Sampling was used to sample the highest probability density (HPD) region of the posterior and determine the maximum of the a posteriori (MAP) for each thermodynamic parameter included. The optimized microkinetic models for each of the three estimation methods had quite similar mechanisms and agreed well with the experimental data for gas phase mole fractions. Exploration of the HPD region of the posterior further revealed that adsorbed hydroxide and oxygen likely bind on facets other than Rhodium 111. The demonstrated workflow addresses the issue of inaccuracies arising from the integration of data from multiple sources by considering both experimental and computational uncertainties, and further reveals information about the active site that would not have been discovered without considering the posterior.
准确而完整的微观动力学模型（MKMs）对于预测复杂化学系统在不同操作条件下的行为非常有效。在多相催化中，它们还可进一步用于新催化剂的快速开发和筛选。密度泛函理论（DFT）通常用于以相对较高的精度计算MKMs中使用的参数。然而，鉴于多相催化中吸附质的DFT计算成本高昂，人们开发了线性标度关系（LSRs）和机器学习（ML）模型来快速估算MKMs中的参数。无论采用哪种方法，很少有研究尝试量化催化MKMs中的不确定性，因为这些不确定性通常比气相模型的不确定性大几个数量级。本研究探讨了通过DFT、LSRs和GemNet-OC（在开放催化剂项目下开发的一种ML模型）计算的热力学参数的不确定性量化和贝叶斯参数估计。选择了一个铑上甲烷催化部分氧化（CPOX）的模型作为案例研究，其中使用DFT、LSR和GemNet-OC确定了该模型的热力学参数及其相关不确定性。马尔可夫链蒙特卡罗结合系综切片采样用于对后验的最高概率密度（HPD）区域进行采样，并确定所包含的每个热力学参数的后验最大值（MAP）。三种估计方法各自优化后的微观动力学模型具有相当相似的机理，并且与气相摩尔分数的实验数据吻合良好。对后验HPD区域的探索进一步表明，吸附的氢氧化物和氧可能吸附在除铑111以外的晶面上。所展示的工作流程通过考虑实验和计算的不确定性，解决了因整合来自多个来源的数据而产生的不准确问题，并进一步揭示了有关活性位点的信息，而不考虑后验就不会发现这些信息。
