We develop computational methods to enable predictive materials synthesis, thus accelerating their design beyond screening. Using a range of tools - from databases to machine learning - we propose solutions in energy, materials theory, and AI. Our group is based in the Department of Materials Science and Engineering at UCLA. Learn more about our research at our website.
Some of our current projects include:
- mkite: a platform for high-throughput materials simulations in distributed computing.
- QUESTS: Quick Uncertainty and Entropy via STructural Similarity
- DM2: Diffusion Model for Disordered Materials (DM2), a generative model capable of producing amorphous structures
- AutoIR: package to simulate IR spectra of organic liquids with OpenFF
