Machine Learning for Materials Discovery

Developing machine-learning interatomic potentials for amorphous materials, and applying ML to accelerate large-scale simulations and materials-property prediction. The goal is to extend first-principles accuracy to system sizes and timescales that brute-force DFT cannot reach.

Detailed write-up coming soon — see Publications and Talks for current results.