Fast screening of homogeneous catalysis mechanisms using graph-driven searches and approximate quantum chemistry
Fast screening of homogeneous catalysis mechanisms using graph-driven searches and approximate quantum chemistry
复制标题
DOI：
10.1039/c9cy01997a
复制
发表时间：
2019-11-21
影响因子：
5
通讯作者：
Habershon, Scott
中科院分区：
文献类型：
--
作者：
Robertson, Christopher;Habershon, Scott
用于预测多步反应机理的计算方法，例如在有机金属配合物的均相催化中发现的方法，正在迅速成为支持实验机理洞察的强大工具。我们最近展示了如何成功地使用图形驱动的采样方案来提出一系列纳米颗粒催化的候选反应机制；然而，在有效的方案中确定该候选集中最有可能的反应机制仍然是一个挑战。在这里，我们展示了使用快速半经验量子化学计算的每个反应路径的简单描述符如何能够识别该机制，但前提是同时考虑所提出的反应机制的热力学和动力学参数。钴催化烯烃加氢甲酰化的成功应用可用于对该策略进行基准测试，并提供对剩余算法挑战的见解。
Computational methods for predicting multi-step reaction mechanisms, such as those found in homogeneous catalysis by organometallic complexes, are rapidly emerging as powerful tools to support experimental mechanistic insight. We have recently shown how a graph-driven sampling scheme can be successfully used to propose a series of candidate reaction mechanisms for nanoparticle catalysis; however, identifying the most-likely reaction mechanism amongst this candidate set in an efficient scheme remains a challenge. Here, we show how simple descriptors for each reaction path, calculated using quick semi-empirical quantum chemistry, enable identification of the mechanism, but only if one considers both thermodynamic and kinetic parameters of proposed reaction mechanisms. Successful application to cobalt-catalysed alkene hydroformylation is used to benchmark this strategy, and provides insight into remaining algorithmic challenges.