Meet CTC: Xuelan Wen
January 19, 2021 -- “Never lose faith in yourself.” This is an important phrase to Xuelan Wen, who is about to start the next big step of her academic career. Xuelan is a recent alum from the Goodpaster group after defending her thesis entitled: "Projection-based Quantum Embedding for Excited States in Molecules and Solids” on December 3. She will soon start a postdoctoral researcher position with Prof. Emily Carter at Princeton University.
While in the Chemical Theory Center, Xuelan worked on WF-in-DFT quantum embedding for method development. She also worked with several experimental groups in the Department of Chemistry, including the Tonks group, the Carlson group, and the Frontiera group.
Xuelan learned Python programming, Github, and various electronic structure methods from the quantum embedding project. She also learned about inorganic, biochemical, and spectroscopic chemistry from her collaborators. She adds, “Most importantly, I learned how to work and communicate with people with different backgrounds. These skills not only help me become a better scientist, they also prepare me to be an effective communicator.”
In her spare time, Xuelan enjoys various outdoor activities, including fishing, biking, and volleyball, with biking being her favorite. “There are so many beautiful and well-maintained bike trails in Minnesota. In summer, I spend most Sunday mornings biking on the 30-mile Grand Rounds Scenic Byway.”
As her time with the Chemical Theory Center comes to an end, she reflects on her time with the Goodpaster group and the University of Minnesota in the following Q&A.
Why did you choose the University of Minnesota for your graduate degree, and what led you to join the Goodpaster group?
The University of Minnesota has numerous, diverse, and strong theoretical chemistry faculty members. I was uncertain about my research interests in my first year, so having the opportunity to choose between various research areas attracted me. I decided to join the Goodpaster group because I didn’t want to miss the opportunity to be one of the first students in a new group. I was hoping to get hands-on instructions from my advisor and the freedom to work on my independent research ideas. I achieved both of these goals.
What was your favorite part of living in Minnesota?
The summer in Minnesota is my favorite part. The state is known as the "Land of 10,000 Lakes,” and there is a park for almost every lake. I go fishing, biking, hiking, barbecuing, camping, and canoeing every summer. If you like outdoor activities, you will love the summer in Minnesota as much as I do.
How did you become interested in studying chemistry, and what gets you the most excited about your field?
I like all scientific areas because they all reveal the rules of the universe. However, I like chemistry the most because chemistry can create new compounds using these rules. Chemists can design new drugs, materials, and catalysts to meet the needs of this unceasingly evolving world.
I’m most excited about the possible advancements in computational chemistry motivated by the flourishing development of computer science algorithms and hardwares. Artificial intelligence greatly improves the efficiency of computer-aided drug design: AlphaFold—an artificial intelligence program developed by Google's DeepMind—triumphs at solving protein structures, and machine-learning potentials enable large-scale ab initio molecular dynamics simulations. The possibility of using computational chemistry to simulate real-world problems with chemical accuracy is inspiring.
What do you enjoy most about your research? What has been your most interesting or surprising finding while in the Chemical Theory Center?
What I enjoy most about my research is working on method development and application projects at the same time. Creating new computational tools and applying existing computational tools to solve real-world problems are equally important and interesting for me.
My most interesting finding so far is from a collaboration with the Tonks group. We combined experiment and computation to study the reaction mechanism of Ti-catalyzed pyrrole synthesis. The experimental rate law involves four compounds with different reaction orders. The proposed mechanism includes more than 10 steps and has many possible ligand combinations. It’s astounding to me that our computational results can interpret the experimental results so well for such complex reactions.
What are you most proud of about your career so far, and what’s one thing you’d like to achieve in the future?
I was very nervous and afraid to give any public talks in my first and second years of graduate school. With support from my advisor and group members, I stepped outside of my comfort zone. I practiced my pronunciation and presentation skills a lot by attending many symposiums and conferences. Now I can give public presentations confidently and fluently at national conferences.
In the future, I would like to aid in drug discovery. I want to combine the knowledge from computational chemistry, artificial intelligence, analytical chemistry, and medicinal chemistry to reveal nature’s secrets.
What are you most excited about in your new position?
I’ve been working on the development of quantum embedding. Most of the time, I worked with relatively simple chemical systems and compared my results with the computational benchmarks. However, I will be working on realistic and complex chemical reactions in my new position, along with comparing my results with the experimental benchmarks. I am excited to see how computation can help illuminate the underlying experimental mechanisms, and how experiment can guide the development of more robust embedding algorithms.