The ambition to relate intrinsic features of chemical data to the underlying chemical reaction networks (CRNs) is not new, but has experienced only modest success. This may partly be attributed to a lack of theoretical groundwork connecting idealized theory to actual experimental data with added complexity. In particular: i) many CRNs have species that cannot be directly observed experimentally; ii) the apparent number of underlying reactions is a function of the resolution of the data; iii) chemical phenomena can change the number of discernable independent processes of the data. In this work, we illustrate the application of the recently introduced concept of data dimension, which quantifies the linearly independent dimensions the system composition in a CRN can change. We perform case studies inspecting the dimensionality of chemical data characterizing CRNs, and outline how it can be used for mechanistic interpretation. In some instances, these extended considerations allow us to directly recover the CRN proposed in the literature without any fitting. This demonstrates that, with incomplete information, important clues about CRN structure can still be recovered. Additionally, our approach detects critical subtleties, preventing important candidate reactions from being discarded in mechanistic studies.

中文翻译：

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化学数据维度的案例研究


将化学数据的内在特征与潜在的化学反应网络 （CRN） 联系起来的雄心壮志并不新鲜，但只取得了适度的成功。这可能部分归因于缺乏将理想化理论与实际实验数据联系起来的理论基础，这增加了复杂性。特别是：i） 许多 CRN 具有无法通过实验直接观察的物种;ii） 潜在反应的表观数量是数据分辨率的函数;iii） 化学现象可以改变数据中可辨别的独立过程的数量。在这项工作中，我们说明了最近引入的数据维度概念的应用，该概念量化了 CRN 中系统组成可以改变的线性独立维度。我们进行了案例研究，检查了表征 CRN 的化学数据的维度，并概述了如何将其用于机理解释。在某些情况下，这些扩展的考虑使我们能够直接恢复文献中提出的 CRN，而无需任何拟合。这表明，在信息不完整的情况下，仍然可以恢复有关 CRN 结构的重要线索。此外，我们的方法可以检测关键的细微差别，防止重要的候选反应在机制研究中被丢弃。