We introduce KiNetX, a fully automated meta-algorithm for the kinetic analysis of complex chemical reaction networks derived from semiaccurate but efficient electronic structure calculations. It is designed to (i) accelerate the automated exploration of such networks and (ii) cope with model-inherent errors in electronic structure calculations on elementary reaction steps. We developed and implemented KiNetX to possess three features. First, KiNetX evaluates the kinetic relevance of every species in a (yet incomplete) reaction network to confine the search for new elementary reaction steps only to those species that are considered possibly relevant. Second, KiNetX identifies and eliminates all kinetically irrelevant species and elementary reactions to reduce a complex network graph to a comprehensible mechanism. Third, KiNetX estimates the sensitivity of species concentrations toward changes in individual rate constants (derived from relative free energies), which allows us to systematically select the most efficient electronic structure model for each elementary reaction given a predefined accuracy. The novelty of KiNetX consists in the rigorous propagation of correlated free-energy uncertainty through all steps of our kinetic analyis. To examine the performance of KiNetX, we developed AutoNetGen. It semirandomly generates chemistry-mimicking reaction networks by encoding chemical logic into their underlying graph structure. AutoNetGen allows us to consider a vast number of distinct chemistry-like scenarios and, hence, to discuss the importance of rigorous uncertainty propagation in a statistical context. Our results reveal that KiNetX reliably supports the deduction of product ratios, dominant reaction pathways, and possibly other network properties from semiaccurate electronic structure data.

中文翻译：

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嘈杂化学反应网络的机理推论

我们引入KiNetX，这是一种全自动的元算法，用于对源自半准确但有效的电子结构计算的复杂化学反应网络进行动力学分析。它旨在（i）加速对此类网络的自动探索，以及（ii）在基本反应步骤的电子结构计算中应对模型固有的错误。我们开发并实现了KiNetX，使其具有三个功能。首先，KiNetX评估（迄今尚未完成的）反应网络中每个物种的动力学相关性，以将新的基本反应步骤的搜索范围仅限于那些认为可能相关的物种。第二，KiNetX识别并消除所有与动力学无关的物种和基本反应，从而将复杂的网络图简化为可理解的机制。第三，KiNetX估计了物种浓度对各个速率常数变化的敏感度（源自相对自由能），这使我们能够为给定预定精度的每个基本反应系统地选择最有效的电子结构模型。KiNetX的新颖之处在于，通过我们动力学分析的所有步骤，相关自由能不确定性的严格传播。检查KiNetX的性能，我们开发了AutoNetGen。通过将化学逻辑编码到其基础的图形结构中，它半随机生成模拟化学反应的网络。AutoNetGen使我们能够考虑大量不同的类似化学的情况，因此，我们讨论了在统计环境中严格传播不确定性的重要性。我们的结果表明，KiNetX可靠地支持从半准确的电子结构数据中推断出产品比率，主要反应路径以及可能的其他网络特性。