Workshops

Advancing molecular simulation with deep learning

Symbolic Regression for Discovery of a DFT Functional

OpenFold: Lesson learned and insights gained from rebuilding and retraining AlphaFold2

Coarse-graining classical and quantum systems

Design and Evaluation of Foundation Models and Generative AI in Molecular Space

Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics

End-to-end learning and auto-differentiation: forces, uncertainties, observables, trajectories and scales.

Data-Driven Design for Energy Materials

Virtual Talk: Diffusion based distributional modeling of conformers, blind docking and proteins

From A to B via a synthesis of rare-event sampling and machine learning

Learning and Emergence in Molecules and Brains

Graph MLP-Mixer for Molecular Analysis

Fully Quantum (Bio)Molecular Simulations: Dream or Reality?

Accurate global machine learning force fields for molecules with hundreds of atoms

Lab-in-the-loop de novo antibody design - what are we missing from machine learning?

Symmetry’s made to be broken: Learning how to break symmetry with symmetry-preserving neural networks

Virtual Talk: Exploring new molecular representations for the design of biological function

Pathways in classification space - Machine learning collective variables for enhanced sampling of structural transformations

Connections and cross pollination from quarks to the cosmos

Incorporating Symmetry for Learning Spatiotemporal Dynamics

Exploring the Intersection of Machine Learning and Electron Microscopy: Advances in Automated Experiments and Physics Discovery

Virtual Talk: Leveraging small datasets for molecular machine learning

Virtual Talk: Go EAST, young scientist - First principles view on chemical compound space