Machine Learning Classification of Disrotatory IRC and Conrotatory Non-IRC Trajectory Motion for Cyclopropyl Radical Ring Opening

09 February 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Transition-state features from trajectories were used for supervised machine learning analysis of the cyclopropyl radical ring opening reaction. Quantitative and qualitative assessment of features controlling disrotatory IRC versus conrotatory non-IRC motion and revealed that there are two key vibrational modes where their directional combination provides prediction of pathway motion.

Keywords

Machine Learning
Quasiclassical dynamics
Quasiclassical trajectories
Density functional theory
cyclopropyl radical
IRC
non-IRC
Post-transition state bifurcation

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.