Date Thesis Awarded


Access Type

Honors Thesis -- Open Access

Degree Name

Bachelors of Science (BS)




Nathanael M. Kidwell

Committee Members

Randolph A. Coleman

Dana L. Willner

Daniel P. Tabor


Density functional theory (DFT) has become a popular method for computational work involving larger molecular systems as it provides accuracy that rivals ab initio methods while lowering computational cost. Nevertheless, computational cost is still high for systems greater than ten atoms in size, preventing their application in modeling realistic atmospheric systems at the molecular level. Machine learning techniques, however, show promise as cost-effective tools in predicting chemical properties when properly trained. In the interest of furthering chemical machine learning in the field of atmospheric science, I have developed a training method for predicting cluster energetics of newly characterized nitrogen-based brown carbon aerosols that can undergo tautomerization. By creating a training dataset of cluster fragment and functional group DFT calculations, I can effectively train machine learning models to predict overall energetics of previously unknown brown carbon clusters while improving computational efficiency.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.