Scientific AI & Computational Chemistry Consulting

Use quantum chemistry and molecular simulation to understand how molecular and crystal structure shapes stability, reactivity and function. Link calculated properties and host–guest interactions with experimental evidence, providing a mechanistic foundation for materials discovery and synthesis decisions.

Connect structures and predicted properties to experimental synthesis. Extract and organise chemical knowledge from literature, then combine it with crystal-structure data, computational screening and machine learning to identify plausible precursors and conditions. Prioritise candidates for validation in a digital chemistry workflow from computation to the lab.

Investigate porous materials, including metal–organic and covalent organic frameworks, for CO₂ capture, gas separation and hydrogen storage. Model pore accessibility and gas–framework interactions to compare candidates, interpret performance and guide attention towards materials that can be made and tested.

Screen porous electrode materials for interactions with battery-relevant ions and clusters, then examine electronic, transport and electrochemical properties. Use the results to explain structure–property relationships and select promising candidates for synthesis and further evaluation.