Department of Chemistry and Centre for Scientific Computing, University of Warwick , Gibbet Hill Road, Coventry CV4 7AL, United Kingdom.
J Chem Theory Comput. 2016 Apr 12;12(4):1786-98. doi: 10.1021/acs.jctc.6b00005. Epub 2016 Mar 24.
In a recent article [ J. Chem. Phys. 2015 , 143 , 094106 ], we introduced a novel graph-based sampling scheme which can be used to generate chemical reaction paths in many-atom systems in an efficient and highly automated manner. The main goal of this work is to demonstrate how this approach, when combined with direct kinetic modeling, can be used to determine the mechanism and phenomenological rate law of a complex catalytic cycle, namely cobalt-catalyzed hydroformylation of ethene. Our graph-based sampling scheme generates 31 unique chemical products and 32 unique chemical reaction pathways; these sampled structures and reaction paths enable automated construction of a kinetic network model of the catalytic system when combined with density functional theory (DFT) calculations of free energies and resultant transition-state theory rate constants. Direct simulations of this kinetic network across a range of initial reactant concentrations enables determination of both the reaction mechanism and the associated rate law in an automated fashion, without the need for either presupposing a mechanism or making steady-state approximations in kinetic analysis. Most importantly, we find that the reaction mechanism which emerges from these simulations is exactly that originally proposed by Heck and Breslow; furthermore, the simulated rate law is also consistent with previous experimental and computational studies, exhibiting a complex dependence on carbon monoxide pressure. While the inherent errors of using DFT simulations to model chemical reactivity limit the quantitative accuracy of our calculated rates, this work confirms that our automated simulation strategy enables direct analysis of catalytic mechanisms from first principles.
在最近的一篇文章[J. Chem. Phys. 2015, 143, 094106]中，我们引入了一种新的基于图的采样方案，可用于高效且高度自动化地生成多原子体系中的化学反应路径。这项工作的主要目的是展示如何将这种方法与直接动力学建模相结合，用于确定复杂催化循环的机制和唯象速率定律，即乙烯的钴催化氢甲酰化反应。我们的基于图的采样方案生成了 31 种独特的化学产物和 32 种独特的化学反应途径；当与自由能的密度泛函理论（DFT）计算和由此产生的过渡态理论速率常数相结合时，这些采样结构和反应途径可用于自动构建催化体系的动力学网络模型。在一系列初始反应物浓度下对该动力学网络进行直接模拟，可以自动确定反应机制和相关速率定律，而无需在动力学分析中预先假定机制或进行稳态近似。最重要的是，我们发现这些模拟中出现的反应机制与 Heck 和 Breslow 最初提出的机制完全一致；此外，模拟的速率定律也与先前的实验和计算研究一致，表现出对一氧化碳压力的复杂依赖性。虽然使用 DFT 模拟来模拟化学反应的固有误差限制了我们计算速率的定量准确性，但这项工作证实了我们的自动模拟策略能够从第一性原理直接分析催化机制。
