🐂 I’m a PhD student in (Prof. Jiayu Peng’s group) at the University at Buffalo, where we develop physics-informed, data-driven machine learning methods (GNN) to reveal the underlying principles of materials thermodynamics and surface kinetics. Our goal is to enable materials-centric AI solutions for chemical transformation and energy technologies through atomistic simulations and characterization data.

🦫 Also, I'm working as a Research Associate in (Prof. Sam Peng’s group) at MIT’s Department of Chemistry, where I led an independent project on lanthanide-doped upconversion nanocrystals (UCNPs), applying Judd–Ofelt theory and ab initio calculations to study radiative/non-radiative transitions of doped ions in a nano-crystal. So far I have developed a Monte Carlo–ODE hybrid computational framework to simulate energy transfer dynamics in multi-ion systems (~3,000 ions), achieving strong agreement with experimental data. This work supports for the rational design of brighter, smaller, and more efficient UCNPs as next-gen bioimaging applications, such as single-molecule tracking under NIR laser excitation in living cell.

🦫 Mentorship & Collaboration

I actively contributed to the lab's research capacity by mentoring junior members and integrating theoretical and computational methods into collaborative projects.

• Master's Thesis Supervision: Yuxuan Zheng, MIT, Investigation of the energy transfer network in upconverting nanoparticles.

Find more of my published work on my Google Scholar. I am open to new collaboration opportunities in computational materials(catalyst design), graph neural network. Feel free to reach out!