Suvarna Manu, Laplaza Rubén, Graux Romain, López Núria, Corminboeuf Clémence, Jorner Kjell, Pérez-Ramírez Javier
Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland.
NCCR Catalysis, 8093 Zurich, Switzerland.
ACS Catal. 2025 Apr 18;15(9):7296-7307. doi: 10.1021/acscatal.5c00412. eCollection 2025 May 2.
Volcano plots, stemming from the Sabatier principle, visualize descriptor-performance relationships, allowing rational catalyst design. Manually drawn volcanoes originating from experimental studies are potentially prone to human bias as no guidelines or metrics exist to quantify the goodness of fit. To address this limitation, we introduce a framework called SPOCK (systematic piecewise regression for volcanic kinetics) and validate it using experimental data from heterogeneous, homogeneous, and enzymatic catalysis to fit volcano-like relationships. We then generalize this approach to DFT-derived volcanoes and evaluate the tool's robustness against noisy kinetic data and in identifying false-positive volcanoes, i.e., cases where studies claim a volcano-like relationship exists, but such correlations are not statistically significant. Once the SPOCK's functional features are established, we demonstrate its potential to identify descriptor-performance relationships, exemplified via the ceria-promoted water-gas shift and single-atom-catalyzed electrocatalytic carbon dioxide reduction reactions. In both cases, the model uncovers descriptors previously unreported, revealing insights that are not easily recognized by human experts. Finally, we showcase SPOCK's capabilities to formulate multivariable descriptors, an emerging topic in catalysis research. Our work pioneers an automated and standardized tool for volcano plot construction and validation, and we release the model as an open-source web application for greater accessibility and knowledge generation in catalysis.
基于萨巴蒂尔原理的火山图可视化了描述符与性能之间的关系，有助于进行合理的催化剂设计。源于实验研究的人工绘制的火山图可能容易受到人为偏差的影响，因为不存在量化拟合优度的指导方针或指标。为了解决这一局限性，我们引入了一个名为SPOCK（火山动力学系统分段回归）的框架，并使用来自多相、均相和酶催化的实验数据对其进行验证，以拟合类似火山的关系。然后，我们将这种方法推广到基于密度泛函理论（DFT）得出的火山图，并评估该工具在处理噪声动力学数据以及识别假阳性火山图方面的稳健性，即研究声称存在类似火山的关系，但这种相关性在统计上并不显著的情况。一旦确定了SPOCK的功能特性，我们就通过二氧化铈促进的水煤气变换反应和单原子催化的电催化二氧化碳还原反应举例，展示其识别描述符与性能关系的潜力。在这两种情况下，该模型都发现了以前未报道的描述符，揭示了人类专家不易识别的见解。最后，我们展示了SPOCK在制定多变量描述符方面的能力，这是催化研究中的一个新兴课题。我们的工作开创了一种用于火山图构建和验证的自动化和标准化工具，并且我们将该模型作为一个开源网络应用程序发布，以便在催化领域更易于获取并促进知识生成。
