Learning Geometry and 3D Symmetries - Day 1
Explore geometry and 3D symmetries in machine learning for drug discovery, covering key concepts like tensor products and network equivalence.
Chemically Transferable Generative Backmapping of Coarse-Grained Proteins
Explore innovative machine learning techniques for generative backmapping of coarse-grained proteins, focusing on transferability, reliability, and efficiency in molecular simulations.
Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design
Explore equivariant 3D-conditional diffusion models for molecular linker design in drug discovery. Learn about DiffLinker's innovative approach to connecting multiple fragments and its applications in real-world scenarios.
EquiReact: An Equivariant Neural Network for Chemical Reactions
Explore EquiReact, an equivariant neural network for predicting chemical reaction properties using 3D structures. Learn its advantages in flexibility, extrapolation, and data efficiency.
Harnessing the Properties of Equivariant Neural Networks for Materials Science
Explore equivariant neural networks for materials science, covering applications, advantages, and future directions in understanding and designing atomic systems.
Unsupervised Learning of Group Invariant and Equivariant Representations
Explore unsupervised learning of group invariant and equivariant representations. Gain insights into symmetry in data, practical examples, and results for graphs and various symmetries.
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
Explore higher-order equivariant message passing neural networks for fast, accurate force fields in computational chemistry. Learn about MACE's innovative approach using four-body messages to enhance efficiency and accuracy.
Clifford Group Equivariant Neural Networks
Explore Clifford Group Equivariant Neural Networks for E(n)-equivariant network construction. Discover theoretical foundations, methodology, and applications in various dimensions and tasks.
Continuous Kendall Shape Variational Autoencoders
Explore unsupervised learning of geometric representations using equivariant VAEs with hyperspherical latents, extending Kendall-shape VAEs for improved representation capacity.
Learning Equivariant Non-Local Electron Density Functionals
Delve into advanced machine learning approaches for improving density functional theory through equivariant graph neural networks, focusing on non-local exchange-correlation functionals and molecular energy predictions.