I work at the intersection of physics simulation and machine learning building tools that predict and validate the properties of new materials before they're ever synthesised. My current focus is comparing density functional theory against ML-driven predictions for perovskite candidates, using physics-grounded corrections to close the gap between fast approximate models and expensive first-principles calculations.
Languages & Core Tools
Computational Physics / DFT
Data & Machine Learning
PEROVOWL Corpus
