Meet CTC: Jingyi Chen

Jingyi Chen Lab

February 25, 2020 -- Jingyi Chen is a fifth-year graduate student in the Siepmann group. She obtained her bachelor’s degree at Nanjing University in China.

Jingyi’s thesis project is supported by the Office of Naval Research under the Multidisciplinary University Research Initiative (MURI) program. The overall goal of this project is to develop high-fidelity physical models and computational methods for predicting multi-phase flow. When a surface or submerged ship is in a turbulent ocean environment, cavitation occurs near propeller blades due to low pressure, forming a large number of bubbles. Subsequent bubble collapses can generate strong shock waves and high local temperatures, which can cause damage to blades. Being able to understand and predict cavitating flows is crucial for improving the ship propeller and hydropower turbine’s performance and longevity.

Since cavitating flow is considerably complex and involves various physical phenomena with a large span in spatial and temporal scales, this project involves collaborative efforts from multiple computational and experimental fluid dynamics groups. Molecular simulations are being used as a tool to reduce empirical parameters and equations utilized in continuum simulations. In addition, some physical phenomena are inherently at molecular scale, such as nucleation and the end stage of bubble collapses.

jingyi with cat

For her research, she uses both force-field-based and first principles Monte Carlo and molecular dynamics simulations. In terms of scientific software, she uses Monte Carlo for Complex Chemical Systems‒Minnesota (MCCCS-MN), Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), and CP2K, which is a quantum chemistry and solid state physics software package.

Outside of her research, Jingyi enjoys reading novels, especially those that feature strong female characters, and playing with her cat, Chili. Jingyi’s favorite part about the Twin Cities is its diverse community. She especially likes Dinkytown, which is a neighborhood near the University of Minnesota-Twin Cities campus, because it has a lively environment and lots of restaurants nearby. 

Jingyi has won multiple awards and honors at the University of Minnesota, including the 2019 Molecular Modeling and Simulation Excellence Fellowship, the 2017 Excellence Fellowship, and the 2016 Outstanding First-Year GPA Award, all from the Department of Chemistry. She also placed second in the 9th Industrial Fluid Properties Simulation Challenge in 2016.

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

The part I enjoy most about my research is that I get to talk to, learn from, and connect with people from different fields. 

One of the most interesting things we have found so far is that although occurence of a negative pressure in a local region is deemed impossible by most of the researchers from computational fluid dynamics, molecular simulation results demonstrate its existence and relevance even at the micron scale, which is large for chemistry. This finding could fundamentally change their physical models. We are looking forward to seeing how this finding can be implemented in computational fluid dynamics simulations.

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 it has a strong and diverse faculty group in computational chemistry. I joined Prof. Siepmann’s group because I was interested in Monte Carlo simulations, and he is a renowned expert in this area.

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

I became interested in the field after I realized that chemistry allows me to view the world at a microscopic scale, which is completely different from how I perceived things before. I became particularly interested in computational chemistry after I learned that the properties of atoms and molecules can be calculated and predicted using theory and computational tools. Overall, computational chemistry helps me gain a deeper understanding of what I learned before in other subfields of chemistry. I am most excited about the increasing computing power these days, which allows me to do larger-scale simulations.

What drives you to be a better scientist?

I am mostly driven by my curiosity and desire to understand things better. I’ve also met some great role models who have inspired me to be as passionate and devoted to science as they are.

What advice do you have for aspiring scientists?

Don’t be afraid of asking for help and advice when you are just getting started, or if you are stuck. Talking can help save a lot of time and avoid unnecessary struggles.