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SPOCK Tool for Constructing Empirical Volcano Diagrams from Catalytic Data.

Manu Suvarna1,2, Rubén Laplaza3,2, Romain Graux4,2

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This study introduces SPOCK (systematic piecewise regression for volcanic kinetics), an automated tool for constructing and validating volcano plots in catalysis. SPOCK reduces human bias and uncovers novel descriptor-performance relationships for rational catalyst design.

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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.