Machine Learning in Predicting Activation Barrier Energy of C=N Bond Rotation in Amides

This study applies machine learning (ML) to predict activation energy barriers for cis‐trans isomerization in twisted amides, focusing on the C=N bond. Amides are increasingly used in synthetic chemistry, particularly in cross‐coupling reactions, due to their versatility. However, the C=N bond’s high activation energy presents a challenge. Using Density Functional Theory (DFT) calculations, the study evaluates key structural parameters and energy barriers for different amides. ML models, including support vector regression and neural networks, are developed to predict these activation barriers based on molecular descriptors. The results show that twisted amides, particularly N‐Ts and N‐Boc types, exhibit unique reactivities influenced by steric and electronic factors. The neural network model outperformed other methods with R2 values of around or over 0.9 for ΔG‡ and ΔGtrans, though overfitting remains a concern. These findings contribute to a deeper understanding of amide reactivity, facilitating the design of more efficient catalytic systems.