Here, we utilize Quantum Chemistry (QC) approaches to predict the structures, vibrational frequencies, infrared intensities and Raman activities of unusual molecular species using the General Atomic and Molecular Structure System (GAMESS(US)) package. A Python-based software, AutoGAMESS, was developed to automate the workflow and take advantage of High Throughput Computing (HTC) techniques enabling the automated generation of spectroscopic data from hundreds of calculations. This approach was utilized to determine these properties for a series of carbon oxides (C2On; n = 3 to 4), anticipated to be produced during the radiation of pure carbon dioxide ices, under conditions relevant to the interstellar medium. Beyond generating predicted spectroscopic results, we additionally performed a benchmark study of 70 different basis sets across multiple levels of theory (including Density Functional Theory, Moller–Plesset, and Coupled Cluster calculations), in QC to identify the method with the best balance between obtaining the lowest error in predictions while being mindful of the computation resources required.
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Master of Science (M.S.)
College of Sciences
Length of Campus-only Access
Masters Thesis (Open Access)
Ferrari, Brian, "Utilizing High Throughput Computing Techniques for the Predictions of Spectroscopic Properties of Astrophysically Relevant Molecules" (2021). Electronic Theses and Dissertations, 2020-. 862.
Restricted to the UCF community until December 2021; it will then be open access.