Published:
I am currently looking for a position in either industry or a faculty position in academia. If you are a recruiter or know of an open position matching my skillset, please message me on LinkedIn or my university email: rtran25@cougarnet.uh.edu
Published:
I’m proud to share that I have been selected as one of the winners of The Southwest Catalysis Society Best Poster Award. My poster, titled High-throughput screening with the Open Catalyst Project explores the application of machine learning in catalysis discovery. The poster specifically focuses on the application of the OC22 framework on the discovery of oxide catalysts for oxygen evolution reaction using high-throughput screening. However, it also provides various examples of how such machine learning potentials can be combined with microkinetic models and the famous Bronsted-Evans-Polanyi relationships to screen for catalyst for other reactions such as: nitrate reduction reaction, methane steam reform, and ethane dehydrogenation to ethylene. Thank you to all the judges at the poster competition, Professor Thomas Senftle for chairing this year’s SWCS 2025, and Professor Lars Grabow and the Grabow Team for your guidance and support in this journey, and congratulations to the other poster winners! Definitely a birthday to remember!
Published:
The interactive Lykoi web app is now live! Check it out on the Lykoi tab above! Lykoi is a toy app but as a side hobby to provide an easy to use interface for designing catalysts and nanoparticles. The Wulff Shape app is built around pymatgen’s wulffshape.py module’s Plotly interface and allows users to input miller indices, surface energies, and lattice parameters to visualize 3D Wulff shapes. The surface phase diagram app allows users to input a .csv file containing surface energies as chemical potentials. A Tutorial tab will be added shortly to the site to provide further instructions on usage. The app is supported by Plotly Dash, pymatgen and the Materials Project API. Future updates will incorporate pre-trained checkpoints from the Open Catalyst Project to allow for on-demand design of catalysts and nanoparticles from ML inference.
