Predictive Chemical Kinetic Modeling: Where We Succeed, Where We Struggle, and What Comes Next.

Area of Science:

• Chemical kinetics
• Computational chemistry
• Reaction modeling

Background:

• Chemical kinetic modeling is crucial for energy, environmental science, pharmaceuticals, and materials.
• Significant progress has been made in gas-phase reaction modeling for thermochemical processes.
• Emerging challenges necessitate extending these methodologies to more complex systems.

Purpose of the Study:

• To review recent advances in chemical kinetic modeling.
• To discuss the application of these methods to challenging systems like heterogeneous catalysis and electrochemistry.
• To advocate for first-principles-based predictions and accessible tools.

Main Methods:

• Review of ab initio transition state theory-based master equation estimation.
• Analysis of automated mechanism generation and machine-learning-assisted kinetics.
• Discussion of uncertainty quantification in kinetic models.

Main Results:

• Systematic methodologies for gas-phase kinetics are established.
• Advances enable potential application to heterogeneous catalysis, electrochemistry, and liquid/solid-state reactivity.
• Need for targeted tools, accessible software, and AI-augmented workflows identified.

Conclusions:

• Future kinetic modeling should prioritize predictive capability and physical accuracy.
• Development of community infrastructure like databases is essential.
• Democratizing modeling through accessible tools will accelerate innovation across diverse scientific fields.