What I do
I am passionate about building production-grade AI models and quantum-mechanical workflows. My focus ispredicting synthesis conditions, crystal structure and materials properties to enable the discovery of novel materials and mechanistic interpretation of complex and unprecedented synthetic outcomes.
Focus Areas
- Text mining of scientific literature to extract synthesis conditions
- Graph Neural Networks for predicting MOF synthesis from 3D crystal structures
- MOF-based battery materials and electrochemistry
- Hydrogen storage and CO2 capture
- Cheminformatics and structure–property relationships
Research Vision
- Deep-tech spinout. Structure-synthesis condition prediction for porous materials (MOFs/COFs)
- Self-driving labs. Closed-loop AI that links simulation, synthesis and characterisation
- Interpretable models that explain unexpected reaction outcomes and guide new chemistry
