A Machine Learning-Guided Study of Structure–Reactivity Relationships in Diels–Alder Cycloadditions
Amir Mahdian, Kaveh Farshadfar, Kari Laasonen
TL;DR
This study uses machine learning and DFT to understand how steric and electronic effects influence Diels–Alder reactions.
Contribution
Introduces a new predictive model combining ML and DFT to analyze structure-reactivity relationships in Diels–Alder reactions.
Findings
Steric effects at internal diene carbons significantly increase activation barriers due to conformational strain.
The minimum energy gap between LUMO and HOMO orbitals is a strong predictor of activation energy.
Steric interactions can cause deviations from the expected reactivity trends based on electronic effects.
Abstract
The Diels–Alder cycloaddition is a cornerstone transformation in organic synthesis and has been extensively studied in both experimental and theoretical contexts. In this work, we present a complementary computational approach that combines density functional theory (DFT) and machine learning to further elucidate the role of steric and electronic effects in determining the reactivity and activation barriers. A diverse dataset of 1000 uncatalyzed hydrocarbon Diels–Alder reactions was used to train predictive models that relate activation energies to chemically meaningful molecular descriptors. SHAP analysis of the machine learning models highlights the dominant influence of steric effects, particularly those associated with substituent volume at the internal diene carbons, which can impose conformational strain and lead to significantly elevated barriers. In contrast, substituents at the…
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Taxonomy
TopicsMachine Learning in Materials Science · Organic Chemistry Cycloaddition Reactions · Cyclization and Aryne Chemistry