Selectivity in organocatalysis—From qualitative to quantitative predictive models

Abstract

Abstract Recent advances in both experimental and computational techniques pose an exciting time for chemistry. Computational tools traditionally used to interpret experimental trends have now evolved into predictive models able to guide the design of novel catalysts. This review discusses the evolution of these models, as well as challenges and future avenues in the field of organocatalysis. Through representative examples we demonstrate how traditional physical organic chemistry tools in combination with machine learning models provide a powerful approach to achieve deeper understanding alongside greater predictive power. This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis Electronic Structure Theory > Density Functional Theory Data Science > Artificial Intelligence/Machine Learning