Organic reaction mechanism classification using machine learning.

Area of Science:

• Catalysis
• Chemical kinetics
• Artificial intelligence in chemistry

Background:

• Mechanistic understanding of catalytic organic reactions is vital for catalyst design and sustainable chemistry.
• Traditional kinetic analysis methods rely on approximations and are limited to simple reaction networks.
• Deriving rate laws can be prone to human error and is challenging for complex systems.

Purpose of the Study:

• To develop an automated method for elucidating catalytic organic reaction mechanisms using kinetic data.
• To overcome limitations of traditional kinetic analysis, including handling complex mechanisms and noisy data.
• To provide a powerful AI tool for streamlining mechanistic elucidation in organic chemistry.

Main Methods:

• Training a deep neural network model on ordinary kinetic data.
• Utilizing the trained model to automatically classify reaction mechanism classes.
• Evaluating the model's performance on various mechanism types, including those out of steady state.

Main Results:

• The deep neural network model accurately identifies diverse reaction mechanism classes.
• The model performs well even with kinetic data containing significant error or few data points.
• It successfully elucidates mechanisms operating outside of steady-state conditions, such as those with catalyst activation/deactivation.

Conclusions:

• AI-guided mechanism classification is a powerful and automatable tool for mechanistic elucidation.
• This approach significantly streamlines the process compared to traditional methods.
• The freely available model is expected to accelerate the development of automated organic reaction discovery.