Samuel Odoh: Developing computing methods to accurately describe and screen chemical compounds

Samuel Odoh

Title

Developing computing methods to accurately describe and screen chemical compounds

Mentor

Samuel Odoh

Department

Chemistry

Bio sketch

Samuel Odoh is an associate professor of Chemistry at UNR. He completed his Ph.D. degree in Computational Chemistry from the University of Manitoba in Canada. Subsequently, he joined the Pacific Northwest National Laboratory (2013) and the University of Minnesota (2013-2016) as a postdoctoral research fellow. He arrived at the University of Nevada, Reno in 2016. The Odoh group develops new quantum-chemical methods for treating strongly correlated systems as well as systems in excited states. These methods will drive the design of new technologies in reaction catalysis and energy generation.

Project overview

In our lab, we develop new computational methods that can allow us to describe the excited state properties of a system, on a computer. Ultimately, if our methods are accurate and precise enough, we will be able to study thousands (and one day, millions) of compounds, without doing any “dirty, dangerous or hands-on experiments. This will allow chemists to screen many possibilities and then focus on the best candidates predicted from theoretical computations. This goal means we have to solve two problems. First, our methods must be very fast, meaning the calculations should finish quickly even for large compounds. Second, our methods must be accurate, meaning we cannot sacrifice accuracy in the interest of speed. In a recent study, we developed a new density functional (DFT) method that performs quite well for many excited state systems. This method adds a “new” ingredient to the class of DFT methods and is broadly applicable to many types of chemical compounds. The goal now is to make it even better and bring it within excellent accuracy of experimental data.

The incoming students will first familiarize themselves with the DFT methods using existing software. Next, they will learn how to add even newer ingredients to DFT methods, to improve its accuracy. Lastly, they will test their newly developed methods on many chemical systems, making sure the newer ingredients make them better, more accurate, without losing too much speed.