Quantum mechanical and machine learning prediction of rotational energy barriers in halogenated aromatic alcohols
DFT calculations were done with Gaussian 16 software at the B3LYP/6-311 + G(d.p) level of theory. Machine learning was done using the classification and regression training (caret) package in R version 4.4.0.
Authors
- Gordon G. Brown (ORCID: https://orcid.org/0000-0001-7408-6714)
- Leah B. Casabianca (ORCID: https://orcid.org/0000-0001-9447-3236)
- Steven T. Cerabona
Institutions
- South Carolina State Governor's Office (US)
- Clemson University (US)
Publication Details
- Journal
- Journal of Molecular Modeling
- Published
- 2025-02-24
- DOI
- https://doi.org/10.1007/s00894-025-06321-y
- Primary Topic
- Molecular Spectroscopy and Structure
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- Clemson University
- Division of Chemistry