Machine learning for predicting product distributions in catalytic regioselective reactions

Area of Science:

• Catalysis
• Organic Chemistry
• Computational Chemistry

Background:

• Achieving predictable selectivity in chemical reactions, particularly enantioselectivity and regioselectivity, remains a significant challenge in catalysis.
• The intricate interplay between reactant molecular features, catalyst properties, and reaction conditions makes it difficult to anticipate reaction outcomes.

Purpose of the Study:

• To apply machine learning tools for analyzing a catalytic regio-selective difluorination reaction of alkenes.
• To decipher the complex, non-linear dependencies between molecular parameters and reaction selectivity.
• To understand how alkene features dictate the formation of 1,1- vs. 1,2-difluorinated products.

Main Methods:

• Utilized machine learning algorithms including neural networks (NN), decision trees (DT), logistic regression (LR), and random forest.
• Applied these models to analyze the outcomes of catalytic regio-selective difluorination reactions.
• Investigated the relationship between alkene structure and the resulting difluorination regioisomer.

Main Results:

• The neural network (NN) model demonstrated high accuracy in predicting whether a given alkene would yield a 1,1- or 1,2-difluorinated product.
• Decision tree (DT) and random forest classifiers provided valuable chemical insights into factors governing regioselectivity.
• The study elucidated the subtle molecular changes that steer the reaction towards specific regioisomers under identical conditions.

Conclusions:

• Machine learning effectively predicts regioselectivity in alkene difluorination, offering a powerful tool for catalyst development.
• The insights gained facilitate rational selection of alkene reactants for targeted regioisomeric products.
• This methodology has the potential to significantly accelerate the discovery and optimization of catalytic transformations.