Predicting Feasible Organic Reaction Pathways Using Heuristically Aided Quantum Chemistry

Area of Science:

• Computational Chemistry
• Organic Chemistry
• Chemical Kinetics

Background:

• Exploring organic reaction mechanisms computationally requires expertise in both organic chemistry and quantum mechanics.
• Full exploration of potential energy surfaces (PES) for organic reactions is computationally infeasible.
• Previous work introduced the heuristically-aided quantum chemistry (HAQC) approach, integrating empirical chemical heuristics with quantum chemical methods.

Purpose of the Study:

• To develop heuristic kinetic feasibility criteria for predicting viable organic reaction pathways.
• To enhance the accuracy and efficiency of computational modeling for complex chemical reactions.
• To establish a unified approach for classifying reaction pathways across diverse organic reaction types.

Main Methods:

• Development of novel heuristic kinetic feasibility criteria.
• Application of these criteria within the heuristically-aided quantum chemistry (HAQC) framework.
• Validation across a range of polar (substitution, addition, elimination) and pericyclic (cyclization, sigmatropic shifts, cycloaddition) organic reactions.

Main Results:

• The developed heuristic kinetic criteria accurately predict feasible reaction pathways for various organic reactions.
• The same set of kinetic heuristics proved effective across diverse reaction mechanisms, unlike knowledge-based methods.
• Demonstrated successful classification of reaction pathways as feasible or infeasible.

Conclusions:

• The heuristic kinetic feasibility criteria offer a robust method for predicting organic reaction mechanisms.
• This approach enhances the computational prediction of chemical reactivity.
• Potential applications in machine learning for chemical reactivity are discussed.