Date of Award
5-2026
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Chemistry
Committee Chair/Advisor
Dr. Carlos D. Garcia
Committee Member
Dr. Christopher Chouinard
Committee Member
Dr. Justin Talbot
Committee Member
Dr. Jorge Barroso
Abstract
This study reports on the incorporation an extreme gradient boosting (XGBoost) machine learning strategy tailored to identifying potential high-energy, insensitive explosive deep eutectic solvents. 1,1-diamino-2,2-dinitroethylene (FOX-7) was investigated as the explosive of interest in our eutectic modeling. The resulting predictions highlighted a wide range of binary compositions demonstrating significant depression from the FOX-7 melting temperature with >70% probabilities of formation. Subsequent computational investigation into select mixtures provided underlying stabilization energies and optimized mixture geometries, which were then assessed experimentally with near simulant compounds oxalyldihydrazide and 1-methyl-3- nitroguanidine replacing FOX-7.
Experimental results with these chosen simulants did not demonstrate classical eutectic behavior. Despite the lack of DES formation, these results yeild valuable insights for refining methodologies in the design of energeti eutectic systems. Although the selected simulants share similarities with FOX-7, they exhibit key deviations in critical physicochemical properties that may hinder their ability to reproduce the predicted eutectic behavior. Accordingly, direct experimental evaluation of the predicted systems with FOX-7 is justified and recommended for future study.
Recommended Citation
Parker, Connor J., "Energetic Eutectics: An Experimental and Computational Investigation on Explosive Deep Eutectic Solvents" (2026). All Theses. 4799.
https://open.clemson.edu/all_theses/4799