Our Research Philosophies
Our guiding research ethos is “extending the successes of atomistic machine-learning up a scale (or two).”
We are interested in leveraging data science and molecular simulation to explore molecular systems with changing and complex phase behavior. This research aims to address pertinent fundamental and applied open problems in computational chemical and materials sciences, all to better our world. Our group is committed to conducting research that upholds the principles of open science and adheres to FAIR data standards.
Areas of Interest
Machine Learning
- For Crystal Structure Prediction: Christian Jorgensen & Grace Lee
- Molecular Representations and Coarse Graining: Arthur Lin & Lucas Ortengren
- Machine Learning Potentials: Natalie Hooven
Molecular Simulation
- For Glasses: Charles Carroll
- For Colloidal Phases: Hwigwang Lim & Caleb Youngwerth
- For Ionic Liquids: Lisa Je
- For Melting: Matthew Reuteman
- For Biological Systems: Zachary Amsterdam
Chemistry as Data
- PCovR/PCovC: Christian Jorgensen & Arthur Lin
- TDA: Ethan Deutsch & Lisa Je