Goal-driven crystal generation
Generative and physics-informed methods for exploring crystal space under stability and synthesizability constraints.
AI for Science Projects
Projects in materials, molecules, reactions, and scientific systems.
A selected set of projects spanning crystal generation, MOF discovery, reaction prediction, molecular interaction modelling, and scientific foundation models.
Related pages
MOFs for CO2 capture
Generative AI and high-throughput screening for metal-organic frameworks tailored to adsorption and selectivity goals.
Chemical reaction prediction
Graph-based and sequential decision approaches to reaction pathways, mechanisms, and transformation structure.
Chemical-chemical interaction
Flexible modelling of multi-molecule interaction, context dependence, and relational structure in complex systems.
Physics-informed GNNs
Structure-aware learning for materials and molecular systems where domain constraints matter.
Foundation models for science
Scientific models and agents that can retrieve, reason, and support discovery workflows across domains.