Date Thesis Awarded
5-2021
Access Type
Honors Thesis -- Open Access
Degree Name
Bachelors of Science (BS)
Department
Chemistry
Advisor
Nathanael M. Kidwell
Committee Members
Randolph A. Coleman
Dana L. Willner
Daniel P. Tabor
Abstract
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.
Recommended Citation
Chappie, Emily E., "Molecular Cluster Fragment Machine Learning Training Techniques to Predict Energetics of Brown Carbon Aerosol Clusters" (2021). Undergraduate Honors Theses. William & Mary. Paper 1622.
https://scholarworks.wm.edu/honorstheses/1622
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