Meet CTC: Daniel Graham

April 16, 2020 -- Daniel Graham is a fourth-year Ph.D. student in the Goodpaster group. Daniel is from Sacramento, California, and he received his undergraduate degrees in chemistry and computer science at Centre College in Danville, Kentucky. 

Daniel’s current project focuses on developing density functional theory (DFT) embedding using the Huzinaga level shift projection operator. He has developed and benchmarked the method for closed-shell systems, and is now working on the open-shell version of the code.

Daniel uses resources from the Minnesota Supercomputing Institute (MSI) to run most of his calculations. He develops in Python utilizing the PySCF computational chemistry package. All of the embedding code is available on the Goodpaster group GitHub (https://github.com/Goodpaster/QSoME). 

Overall, the group aims to solve climate-related problems and reduce energy consumption. Specifically, members are studying carbon dioxide remediation, or pulling carbon dioxide out of the atmosphere. If there is too much carbon dioxide in the atmosphere, it leads to excessive heating. The group is also looking at making the refinement process of natural gas more efficient. Before natural gas can be transported and used, it must be stripped of impurities and various hydrocarbons and fluids. The Goodpaster group wants to make this process use less energy so that natural gas can be a more widely-used alternative to coal or diesel, which emit more greenhouse gases.

Specifically, the Goodpaster group is applying embedding methodology to a variety of metal organic frameworks (MOFs) to understand how different gases bind to the metal centers. These high accuracy embedding results can help refine MOF screening procedures and guide the development of novel MOFs for gas adsorption. 

In Daniel’s spare time, he enjoys music and playing gigs with his band, Boy Crazy… With Their Hit Song, at local bars. If the weather is nice, Daniel likes biking around the Twin Cities. He also coaches a youth track and field team. After completing his Ph.D., Daniel hopes to teach at a liberal arts college. He likes mentoring students and gets excited when he sees someone understand something they didn't know before. 

How did you become interested in studying chemistry, and what gets you the most excited about your field?

I was initially drawn to chemistry because I was, and still am, fascinated by the dramatic changes that can occur in a system over the course of a reaction. Theoretical computational chemistry is exciting to me because I can apply my love of programming in an experimental context. Simulation is an invaluable tool for reducing the cost of chemistry and allowing us to gain new insights about systems.

Why did you choose the University of Minnesota, and what led you to join your current Research Group? 

I chose the University of Minnesota because I knew I wanted to study computational chemistry, and the Department of Chemistry has an abundance of excellent faculty specializing in computational methods. I wasn’t sure specifically what kinds of projects I wanted to work on, but I knew no matter what I decided, I would have an outstanding faculty support network. I chose to join Prof. Goodpaster’s group because I wanted the opportunity to work closely with my advisor. Our group had only a handful of students when I joined, so Prof. Goodpaster is very involved with everyone’s projects.

What do you enjoy most about your research? What has been your most interesting or surprising finding so far?

One of the most satisfying parts of my research is when I fix a bug in my code and the results suddenly look fantastic. It is a rare and elusive experience, but it’s definitely worth the struggle. One of our most interesting findings so far is that when dividing a system across covalent bonds, the embedding results improve by including all bonding electrons in the wavefunction subsystem. As a result, charging schemes that seem unphysical actually result in better accuracy. 

What are you most proud of about your academic career so far, and what’s one thing you’d like to achieve in the future?

So far I am most proud of the QSoME code, the embedding package I have been developing for the past three years. In the future, I hope to see more groups utilizing the package to solve their electronic structure problems. 

What drives you to be a better scientist?

I am driven to keep improving my research by the scientific community at large. Whenever I look at a scientific journal, I am amazed by the depth and scope of our knowledge. All of our scientific progress has been built on the work of previous generations. I want to help build a part of our knowledge edifice.