Abstract
AbstractRecent 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
Highlights
Control over all manner of selectivity—from chemo- and regioselectivity to diastereo- and enantioselectivity—is at the core of current efforts in modern organic chemistry
Quantitative studies often focus on the calculation of reaction mechanisms using density functional theory (DFT) approaches, with a general workflow shown in Figure 5(a) that consists of (i) DFT optimizations of minima and transition states, from which thermodynamic corrections are obtained, (ii) conformational sampling at each identified stationary point; (iii) corrections to electronic energies using more accurate methods; and (iv) calculation of molecular properties, such as the molecular electrostatic potential (MEP), NCI plots,[31] and natural bond orbital (NBO) analysis.[32]
We discuss how machine learning (ML) approaches, including multivariate linear regression (MLR), support vector machines (SVMs), decision trees (DTs), random forests (RFs), and neural networks (NNs),[58,59] have been used to develop predictive selectivity models based on experimental data and molecular descriptors
Summary
Control over all manner of selectivity—from chemo- and regioselectivity to diastereo- and enantioselectivity—is at the core of current efforts in modern organic chemistry.
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