Traversing Dense Networks of Elementary Chemical Reactions to Predict Minimum-Energy Reaction Mechanisms
Traversing Dense Networks of Elementary Chemical Reactions to Predict Minimum-Energy Reaction Mechanisms
复制标题
遍历基本化学反应的密集网络来预测最小能量反应机制
DOI：
10.1002/syst.201900047
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Robertson C
中科院分区：
文献类型：
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作者：
Robertson C
在过去的几年里，已经开发了许多不同的算法，这些算法能够生成描述一组离散分子固有的化学反应活性的大型、密集的化学反应网络。对于给定反应网络中的所有基元反应，反应速率计算，然后是直接的微观动力学模型，使人们能够基于原子输入数据来预测宏观结果(例如， 例如，速率定律，产物选择性)。然而，对于包含数千个反应物分子的化学反应网络来说，这样的模拟可能非常耗时；此外，分子浓度的复杂耦合时间依赖关系在寻求基本的机理特征时可能会带来挑战。在本文中，我们转而提出一种算法，该算法寻求在给定先前生成的反应网络作为输入的情况下，预测连接任何两个用户选择的反应物和产品种类的“最有可能”的反应机制或竞争机制。该方法被成功地用于描述铂纳米颗粒上的一氧化碳氧化的反应网络(包含数万个可能的反应)。
Numerous different algorithms have been developed over the last few years which are capable of generating large, dense chemical reaction networks describing the inherent chemical reactivity of a collection of discrete molecules. For all elementary reactions in a given reaction network, reaction rate calculations, followed by direct micro‐kinetic modelling, enables one to predict macroscopic outcomes (e. g. rate laws, product selectivity) based on atomistic input data. However, for chemical reaction networks containing thousands of reactant molecules, such simulations can be extremely time‐consuming; in addition, the complex coupled time‐dependence of molecular concentrations can present challenges when seeking essential mechanistic features. In this Article, we instead present an algorithm which seeks to predict the “most likely” reaction mechanism, or competing mechanisms, connecting any two user‐selected reactant and product species, given a previously‐generated reaction network as input. The approach is successfully tested for reaction networks (containing tens of thousands of possible reactions) describing the carbon monoxide oxidation on platinum nanoparticles.