Our research group focuses on a central problem in chemistry and engineering: How to quantitatively predict the reactivity of chemical systems?

We develop a new platform technology of autonomous learning systems to predict the behavior of chemical systems. In parallel to developing cutting-edge computational tools, we solve practical problems such as identifying optimal fuel mixtures for desired applications, estimating pharmaceutical drugs shelf life, and designing new degradation-resistant polymers.

Research in our fundamental and applied chemical kinetics group is based on using computational chemistry methods and generating high-quality chemical kinetic models to predict and interpret experimental results across a wide range of different fields of chemistry and engineering. The unifying theme of our work is method development for answering a grand question: “Given known initial conditions, could we predict how a chemical system will evolve with time?”

We enjoy collaborations, and are always open to the challenge of trying to model new chemical systems.

We’re looking for promising graduate (MSc/PhD) students to work on exciting projects. Check out our positions page.