Our work begins with a chemical question rather than a preferred algorithm. We use electronic-structure theory, automated exploration, data-driven models and scientific software in combinations suited to the problem.
Automated exploration
Chemical discovery is often limited by the structures and mechanisms a researcher thinks to test. We are interested in computational strategies that search more broadly: generating molecular structures, navigating reaction pathways and identifying chemically meaningful regions of large search spaces.
Explore PyAR, our programme for automated structure and reaction search →
Molecular aggregates, nanoclusters and nanoalloys
Atoms and molecules can assemble into many competing structures whose stability and properties depend on size, composition, bonding and collective interactions. We use automated cluster building, quantum chemistry and machine-learned potentials to explore these landscapes—from hydrogen-bonded molecular aggregates to metallic clusters and multicomponent nanoalloys.
Explore molecular aggregates, nanoclusters and nanoalloys →
Astrochemical and prebiotic space
Some problems begin by asking which molecules are possible; others ask how those molecules form. HydroMol maps a defined space of small hydrocarbons, while our automated reaction studies explore networks involving HCN, HNC, ammonia, formaldehyde and related species. Astrochemistry and prebiotic chemistry provide demanding tests of both approaches.
Explore automated reaction discovery and astrochemical space →
Molecular discovery and functional materials
We combine molecular generation, quantum chemistry and machine learning to move from large candidate spaces towards molecules selected for particular functions. Current applications include strained hydrocarbons for energy storage, TADF emitters for OLEDs, photocages, photoswitches and photosensitisers.
Explore molecular discovery and functional materials →
Across these themes, machine learning is used when it makes a chemical problem more tractable: to expand a search, accelerate an energy model or identify relationships worth testing. Prediction alone is not enough; the model, data, uncertainty and domain limits must remain visible.
Group research
Detailed projects, publications, software and current group members belong on the AnoopLab website. The independent page on collaborative research explains how experimental questions and shared investigations shaped these research directions. Open-source software and data are maintained through the AnoopLab GitHub organisation.