Catalysis informatics has received tremendous attention in recent years as a tool to design catalysts and discover unique descriptors that capture the relationships between chemical properties and catalytic performance. One of the stop-gaps in understanding catalytic effects, which is often ignored and limits the deployment of data science tools, relates to the lack of uniform data. The catalytic cleavage of C–X (X= H, C, N, and O) bonds is relevant to many fundamental catalytic processes. In this Perspective, we performed data analytics on four groups of C–X cleavage reactions that are common in production, upcycling, or reactive separation: the C–C cleavage in cyclopropyl alcohol, the C–H cleavage in hydroacylation reactions, the C–O cleavage in β-O-4 linkages, and the C–N cleavage in amides, using experimental data collected from the literature to understand their underlying correlations. Experimental variables of high impact are identified for each reaction by dimensionality reduction methods. We highlight the urgent need for experimental data sets that include full details on the reaction conditions, such as reagent concentration, reaction temperature, or time in machine-readable forms. We discuss the potential improvement of the data of these reactions and promising approaches such as autonomous experiments to fill the gaps in unbiased experimental data. We also address the early stage consideration of separation aspects in the experimental design of efficient catalytic systems for these fundamental examples of chemical reactivity.

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催化预测的数据分析：我们准备好了吗？

近年来，催化信息学作为一种设计催化剂和发现捕捉化学性质与催化性能之间关系的独特描述符的工具受到了极大的关注。理解催化效应的权宜之计之一是缺乏统一的数据，这一问题经常被忽视并限制了数据科学工具的部署。 C-X（X= H、C、N 和 O）键的催化裂解与许多基本催化过程相关。在本视角中，我们对生产、升级循环或反应分离中常见的四组 C-X 裂解反应进行了数据分析：环丙醇中的 C-C 裂解、加氢酰化反应中的 C-H 裂解、C- β-O-4 连接中的 O 裂解和酰胺中的 C-N 裂解，使用从文献中收集的实验数据来了解它们的潜在相关性。通过降维方法确定每个反应的高影响实验变量。我们强调迫切需要实验数据集，其中包括反应条件的完整详细信息，例如机器可读形式的试剂浓度、反应温度或时间。我们讨论了这些反应数据的潜在改进和有前途的方法，例如自主实验，以填补无偏见实验数据的空白。我们还针对这些化学反应性的基本示例，解决了高效催化系统实验设计中分离方面的早期考虑。