Molecular Understanding and Practical In Silico Catalyst Design in Computational Organocatalysis and Phase Transfer Catalysis-Challenges and Opportunities

Through the lens of organocatalysis and phase transfer catalysis, we will examine the key components to calculate or predict catalysis-performance metrics, such as turnover frequency and measurement of stereoselectivity, via computational chemistry. The state-of-the-art tools available to calculate potential energy and, consequently, free energy, together with their caveats, will be discussed via examples from the literature. Through various examples from organocatalysis and phase transfer catalysis, we will highlight the challenges related to the mechanism, transition state theory, and solvation involved in translating calculated barriers to the turnover frequency or a metric of stereoselectivity. Examples in the literature that validated their theoretical models will be showcased. Lastly, the relevance and opportunity afforded by machine learning will be discussed.

Automated summary

This article examines the key components for calculating or predicting catalytic performance metrics, such as turnover frequency and stereoselectivity, using computational chemistry in the context of organocatalysis and phase transfer catalysis. State-of-the-art tools for calculating potential energy and free energy, along with their limitations, are discussed through literature examples. The challenges of translating calculated barriers into turnover frequency or stereoselectivity metrics are highlighted through various examples, including mechanism, transition state theory, and solvation. Examples validating theoretical models from the literature are showcased. Finally, the relevance and potential of machine learning are discussed.