# A Machine Learning-Guided Study of Structure–Reactivity Relationships in Diels–Alder Cycloadditions

## 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.

## Key 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 terminal positions have a more limited impact. We introduced
the minimum energy gap between LUMOdiene–HOMOdienophile and LUMOdienophile–HOMOdiene as a key predictive descriptor. This feature shows a strong correlation
with the activation energy across the dataset, although steric interactions
can lead to notable deviations from the overall trend. The resulting
models provide insights for rationalizing selectivity and designing
more efficient cycloadditions based on steric and electronic complementarity.