Abstract by Nick Rollins

Personal Infomation

Presenter's Name

Nick Rollins

Degree Level


Abstract Infomation


Chemistry and Biochemistry

Faculty Advisor

Daniel Ess


Combining Quassiclassical Direct Dynamics and Machine Learning for Thermal Deazetization


Chemical reactions are generally governed by statistical effects that can be accurately described by Eyring’s transition state theory. However, several organic reactions have been identified that have nonstatisical behavior for selectivity. This talk will describe our density-functional theory study using quasiclassical direct dynamics to model the thermal deazetization of 2,3-diazabicyclo[2.2.1]hept-2-ene leading to a dynamical ratio of bicyclic products. Furthermore, classification machine learning algorithms were examined to predict the dynamical product ratio based on vibrational energies and atomic velocities.