Meet AI-Hilbert, a new algorithm for transforming scientific discovery
News
Peter Hess
Physical Sciences
Our physical sciences researchers seek to understand materials and their properties through advanced characterization methods, explore novel devices and materials for new computational platforms, and apply physics to improve AI algorithms and interpretability.
Our work
Resolving the first anti-aromatic carbon allotrope
Technical noteLeo GrossEl Niño may provide an early warning signal for toxic algae blooms on Lake Erie
NewsKim MartineauScanning probe microscopy technique could open the door to molecular machines
ResearchLeo Gross, Florian Albrecht, and Ivano TavernelliComputer simulations identify new ways to boost the skin’s natural protectors
ResearchJason Crain5 minute readThe first atomic-resolution images of extraterrestrial molecules
ResearchLeo Gross and Katharina Kaiser6 minute read- See more of our work on Physical Sciences
Publications
MDLab: AI frameworks for Carbon Capture and Battery Materials
- Bruce Elmegreen
- Hendrik Hamann
- et al.
- 2025
- Frontiers in Environmental Science
Electrical Performance of Hybrid Bonding with Sub-Micron Cu-Cu Bonding Contacts: Effects of Scaling, Microstructure, and Surface Morphology
- Sari Zerey
- Alina Bennett-dubin
- et al.
- 2025
- ECTC 2025
Fast And Accurate Machine Learning Prediction of Back-End-Of-Line Thermal Resistances in Backside Power Delivery and Chiplet Architectures
- Prabudhya Roy Chowdhury
- Aakrati Jain
- et al.
- 2025
- ECTC 2025
How AI can accelerate Materials Discovery for CO2 Capture
- 2025
- Canadian Separations Symposium 2025
Opportunities of Scalable and Electrostatically Optimized Electrodes for Electric Field- and Current-Driven Microfluidic Applications
- 2025
- Biomicrofluidics
A new framework for evaluating model out-of-distribution generalisation for the biochemical domain
- 2025
- ICLR 2025