SPOCK Tool for Constructing Empirical Volcano Diagrams from Catalytic Data

Area of Science:

• Catalysis
• Computational Chemistry
• Materials Science

Background:

• Volcano plots visualize descriptor-performance relationships for catalyst design.
• Manual construction is prone to human bias due to lack of standardization.
• Objective metrics for quantifying the goodness of fit are needed.

Purpose of the Study:

• Introduce SPOCK (systematic piecewise regression for volcanic kinetics) for automated volcano plot analysis.
• Validate SPOCK using diverse experimental and DFT-derived catalytic data.
• Enhance objectivity and reliability in catalysis research.

Main Methods:

• Developed a framework for fitting volcano-like relationships using piecewise regression.
• Validated against heterogeneous, homogeneous, enzymatic, and DFT-derived catalysis data.
• Assessed robustness against noisy data and identified false-positive volcanoes.

Main Results:

• SPOCK accurately fits volcano-like relationships across various catalytic systems.
• The tool effectively identifies statistically significant descriptor-performance correlations.
• Uncovered novel descriptors in ceria-promoted water-gas shift and CO2 reduction reactions.
• Demonstrated capability in formulating multivariable descriptors.

Conclusions:

• SPOCK provides an automated, standardized, and validated approach to volcano plot analysis.
• Enables objective identification of descriptor-performance relationships in catalysis.
• Facilitates rational catalyst design and accelerates knowledge generation through an open-source tool.