The Genesis of Molecular Volcano Plots

Area of Science:

• Computational Chemistry
• Catalysis
• Chemical Reaction Engineering

Background:

• Linear free energy scaling relationships and volcano plots are established computational tools for heterogeneous and electrocatalysis.
• These methods estimate catalytic performance based on descriptor variables and Sabatier's principle.
• The application of volcano plots has recently been extended to homogeneous catalysis.

Purpose of the Study:

• To summarize the implementation and refinement of molecular volcano plots for homogeneous catalysis.
• To demonstrate the validity and utility of volcano plots in analyzing and predicting homogeneous catalyst behavior.
• To explore the integration of machine learning with molecular volcano plots for large-scale catalyst screening.

Main Methods:

• Application of linear free energy scaling relationships to homogeneous catalytic systems (e.g., Suzuki-Miyaura cross-coupling).
• Development of thermodynamic and kinetic molecular volcano plots.
• Integration of machine learning for descriptor variable prediction and big-data analysis of catalytic behavior.

Main Results:

• Proof-of-principle demonstrated the applicability of volcano plots to homogeneous catalysis, establishing thermodynamic relationships.
• Transition from thermodynamic to kinetic volcanoes allowed direct estimation of transition state barriers and turnover frequencies.
• Machine learning integration enabled rapid screening of thousands of catalysts and enhanced understanding through data mining.

Conclusions:

• Molecular volcano plots are a valid and powerful tool for analyzing and predicting homogeneous catalyst performance.
• The methodology has evolved from thermodynamic to kinetic predictions and incorporates machine learning for efficiency.
• Augmented volcano plots offer a new approach by eliminating linear free energy scaling relationships for performance discrimination.