Research Initiation Award: Thermal Decomposition of Four-membered Heterocyclic Peroxides, Data Mining in Nonadiabatic Trajectories, and Chemiexcitation Efficiency

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HBCU-UP’s Research Initiation Awards provide support for STEM faculty to pursue research activities to further their research capabilities and effectiveness and enhance STEM research and undergraduate education at HBCUs. This award to Jackson State University has the potential to improve bio-imagery, train undergraduate students in machine learning and artificial intelligence, and produce education modules in advanced technology for STEM courses. The project aims to use a novel application of machine learning to discern what atoms in a light emitter dominate its chemiexcitation efficiency to improve bio-imaging devices. The significance of this projects is in advancing knowledge of machine learning of thermolysis and assisting in the synthesis of a new generation of light emitters that advances image quality for bioluminescent imaging and improved treatment outcomes for tumor therapy. Student participants will receive training in critical workforce skills. This project seeks to use the Bayesian Support Vector Machine (BSVM) learning model to uncover the origins of high chemiexcitation efficiency in synthesized light emitters. The project is novel in that it (1) expounds the excitation of the trajectories via the conical intersection (CI) topology obtained by optimizing the CI seams around the reference trajectories; 2) projects the energy state of a trajectory at any instant via the BSVM separatrix; 3) searches the most influential features that describe the decision boundary and classifies surface hopping trajectories statistically; 4) characterizes the features that are farthest from the separatrix and predicts the energy states with high probability; 5) establishes the relation between initial phase space coordinates and transition thresholds to facilitate the designing process for new light emitters with high lighting efficiency; and 6) develops multiple small computational modules, each of which will be used as research topics for HBCU undergraduate student research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.