Microkinetic Molecular Volcano Plots for Enhanced Catalyst Selectivity and Activity Predictions

Area of Science:

• Computational catalysis
• Chemical kinetics
• Materials science

Background:

• Molecular volcano plots are established tools for predicting catalyst performance.
• Existing methods lack the ability to incorporate dynamic reaction parameters and complex catalytic scenarios.

Purpose of the Study:

• To develop "microkinetic molecular volcano plots" by integrating microkinetic modeling into the volcano plot framework.
• To create a unified computational tool for predicting catalyst activity and selectivity under various conditions.
• To enable the explicit determination of selectivity and product ratios by tracking concentration evolution.

Main Methods:

• Integration of microkinetic modeling with the molecular volcano plot framework.
• Development of a Python program, "mikimo", for automated plot construction.
• Application to case studies: rhodium-catalyzed hydroformylation and metal-catalyzed hydrosilylation.

Main Results:

• Demonstrated the ability to incorporate temperature, reaction time, and concentration into volcano plots.
• Showcased the framework's capacity to handle complex scenarios like resting states and coupled cycles.
• Highlighted the simultaneous screening of catalysts and reaction conditions for optimized performance.

Conclusions:

• Microkinetic molecular volcano plots provide a more comprehensive understanding of catalytic behavior compared to traditional plots.
• The developed framework allows for explicit determination of selectivity and product ratios.
• The "mikimo" program facilitates the construction of these advanced computational tools for catalyst discovery.