Given the computational resources available today, data-driven approaches can propel the next leap forward in catalyst design. Using a data-driven inspired workflow consisting of data generation, statistical analysis, and dimensionality reduction algorithms we explore trends surrounding the thermodynamics of a model hydroformylation reaction catalyzed by group 9 metals bearing phosphine ligands. Specifically, we introduce “augmented volcano plots” as a means to easily visualize the similarity of each catalyst's complete catalytic cycle energy profile to that of a hypothetical ideal reference profile without relying upon linear scaling relationships. In addition to quickly identifying catalysts that most closely match the ideal thermodynamic catalytic cycle energy profile, these maps also enable a more refined comparison of closely lying species in standard volcano plots. For the reaction studied here, they inherently uncover the presence of multiple sets of scaling relationships differentiated by metal type, where iridium catalysts follow distinct relationships from cobalt/rhodium catalysts and have profiles that more closely match the ideal thermodynamic profile. Reconstituted molecular volcano plots confirm the findings of the augmented volcanoes by showing that hydroformylation thermodynamics are governed by two distinct volcano shapes, one for iridium catalysts and a second for cobalt/rhodium species.

中文翻译：

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用于均相催化的数据驱动的增强火山图

鉴于当今可用的计算资源，数据驱动的方法可以推动催化剂设计的下一次飞跃。使用由数据生成、统计分析和降维算法组成的数据驱动启发工作流程，我们探索了由带有膦配体的第 9 族金属催化的模型加氢甲酰化反应的热力学趋势。具体来说，我们引入“增强火山图”作为一种手段，可以轻松地可视化每种催化剂的完整催化循环能量曲线与假设的理想参考曲线的相似性，而无需依赖线性比例关系。除了快速识别与理想热力学催化循环能量曲线最匹配的催化剂之外，这些图还可以对标准火山图中紧密分布的物种进行更精细的比较。对于此处研究的反应，他们本质上揭示了按金属​​类型区分的多组缩放关系的存在，其中铱催化剂遵循与钴/铑催化剂不同的关系，并且具有更接近理想热力学曲线的曲线。重建的分子火山图证实了增强火山的发现，表明加氢甲酰化热力学受两种不同的火山形状控制，一种是铱催化剂，另一种是钴/铑物质。