Idil Ismail, a current 3rd year HetSys student and her supervisor Scott Habershon have had their latest paper featured as an Editors' Pick in the Journal of Chemical Physics.

To date, several reaction discovery tools have been developed to accelerate and automate mechanistic elucidation. However, despite significant progress in this area, there is no single approach capable of rapidly assessing key kinetic parameters such as activation energies, without resorting to expensive first-principles calculations. In this paper, Idil and Scott show how machine learning (ML) can be used to accelerate chemical discovery pipelines; and evaluate the impact of the uncertainty associated with ML prediction of activation energies on the observable properties of chemical reaction networks based on microkinetic simulations using ML predictions.

You can read the article online here.

You can read more about Idil and Scott's work here.