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首页> 外文期刊>Journal of chemical theory and computation: JCTC >Heuristics-Guided Exploration of Reaction Mechanisms
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Heuristics-Guided Exploration of Reaction Mechanisms

机译:启发式指导的反应机理探索

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For the investigation of chemical reaction networks, the efficient and accurate determination of all relevant intermediates and elementary reactions is mandatory. The complexity of such a network may grow rapidly, in particular if reactive species are involved that might cause a myriad of side reactions. Without automation, a complete investigation of complex reaction mechanisms is tedious and possibly unfeasible. Therefore, only the expected dominant reaction paths of a chemical reaction network (e.g., a catalytic cycle or an enzymatic cascade) are usually explored in practice. Here, we present a computational protocol that constructs such networks in a parallelized and automated manner. Molecular structures of reactive complexes are generated based on heuristic rules derived from conceptual electronic-structure theory and subsequently optimized by quantum-chemical methods to produce stable intermediates of an emerging reaction network. Pairs of intermediates in this network that might be related by an elementary reaction according to some structural similarity measure are then automatically detected and subjected to an automated search for the connecting transition state. The results are visualized as an automatically generated network graph, from which a comprehensive picture of the mechanism of a complex chemical process can be obtained that greatly facilitates the analysis of the whole network. We apply our protocol to the Schrock dinitrogen-fixation catalyst to study alternative pathways of catalytic ammonia production.
机译:为了研究化学反应网络,必须高效,准确地确定所有相关的中间体和基本反应。这种网络的复杂性可能会迅速增长,特别是如果涉及的反应性物种可能会引起大量的副反应。如果没有自动化,对复杂反应机制的完整研究将很乏味,而且可能不可行。因此,在实践中通常仅探索化学反应网络的预期主要反应路径(例如,催化循环或酶促级联反应)。在这里,我们提出了一种计算协议,该协议以并行和自动化的方式构造此类网络。反应性络合物的分子结构是基于从概念性电子结构理论得出的启发式规则生成的,随后通过量子化学方法进行优化,以生成新兴反应网络的稳定中间体。然后自动检测该网络中可能通过基本反应根据某种结构相似性度量关联的一对中间中间体,并对其进行自动搜索以寻找连接过渡状态。将结果可视化为自动生成的网络图,从中可以获取复杂化学过程机理的全面图片,极大地方便了整个网络的分析。我们将我们的方案应用于Schrock双氮固定催化剂,以研究催化氨生产的替代途径。

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