Quantum mechanical and machine learning prediction of rotational energy barriers in halogenated aromatic alcohols.

ABSTRACT:

Context

Rotation about a chemical bond is important in many chemical processes and can be influenced by neighboring substituents on a molecule. Rotational energy barriers can be predicted by density functional theory (DFT) calculations. Here, we specifically explore how substituents influence the barrier to rotation about the C-O bond in symmetrically halogenated aromatic alcohols. A machine learning model was trained on the DFT-calculated rotational energies and was found to do a good job predicting rotational energy barriers from the electronegativity, atomic radius, and Hammett constant for each substituent. The machine learning model was found to perform better when it was trained separately on pyrenols, anthranols, or phenols than when it was trained on all classes of compound

SUBMITTER: Cerabona ST 

PROVIDER: S-EPMC11850414 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature