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Bruno Correia - Exploring new molecular representations for the design of biological function
Presenter
- Bruno Correia
January 26, 2023
IPAM
Tess Smidt - Learning how to break symmetry with symmetry-preserving neural networks - IPAM at UCLA
Presenter
- Tess Smidt
January 26, 2023
IPAM
Jutta Rogal - Machine learning collective variables for sampling of structural transformations
Presenter
- Jutta Rogal
January 26, 2023
IPAM
Kyle Cranmer - Connections and cross pollination from quarks to the cosmos - IPAM at UCLA
Presenter
- Kyle Cranmer
January 26, 2023
IPAM
Rose Yu - Incorporating Symmetry for Learning Spatiotemporal Dynamics - IPAM at UCLA
Presenter
- Rose Yu
January 26, 2023
IPAM
Xavier Bresson - Graph MLP-Mixer for Molecular Analysis - IPAM at UCLA
Presenter
- Xavier Bresson
January 25, 2023
IPAM
Alexandre Tkatchenko - Fully Quantum (Bio)Molecular Simulations: Dream or Reality? - IPAM at UCLA
Presenter
- Alexandre Tkatchenko
January 25, 2023
IPAM
Stefan Chmiela - Accurate global machine learning force fields for molecules with hundreds of atoms
Presenter
- Stefan Chmiela
January 25, 2023
IPAM
Kyunghyun Cho - Lab-in-the-loop de novo antibody design - what are we missing from machine learning?
Presenter
- Kyunghyun Cho
January 25, 2023
IPAM
Steve Brunton - Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics
Presenter
- Steve Brunton
January 24, 2023
IPAM
Rafael Gomez Bombarelli - End-to-end learning and auto-differentiation: forces, uncertainties, etc.
Presenter
- Rafael Gomez-Bombarelli
January 24, 2023
IPAM
Kristin Persson - Data-Driven Design for Energy Materials - IPAM at UCLA
Presenter
- Kristin Persson
January 24, 2023
IPAM