BYU

Abstract by Samuel Pugh

Personal Infomation


Presenter's Name

Samuel Pugh

Degree Level

Undergraduate

Co-Authors

Nick Rollins
Johnny Huang
Jordan Jenkins
Kavika Faleumu
Dan Ess

Abstract Infomation


Department

Chemistry and Biochemistry

Faculty Advisor

Daniel Ess

Title

Using Machine Learning to Predict Dynamical Product Ratios from Quasiclassical Direct Dynamics Simulations

Abstract

This talk will outline the use of machine learning algorithms to predict selectivity in the diazotization of 2,3-diazabicyclo[2.2.1]hept-2-ene. The use of our in house quasiclassical direct dynamics program DynSuite to model dynamic trajectories and record vibrational energies and atomic velocities will be discussed. Finally, feature selection, and the efficacy of various machine learning algorithms to predict product ratios for this reaction will be reported.