Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution

no code implementations • 3 Oct 2023 • Rui Wang, Elyssa Hofgard, Han Gao, Robin Walters, Tess E. Smidt

Modeling symmetry breaking is essential for understanding the fundamental changes in the behaviors and properties of physical systems, from microscopic particle interactions to macroscopic phenomena like fluid dynamics and cosmic structures.

Super-Resolution

E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials

2 code implementations • 8 Jan 2021 • Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, Boris Kozinsky

This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations.

Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties

1 code implementation • 19 Aug 2020 • Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé

Equivariant neural networks (ENNs) are graph neural networks embedded in $\mathbb{R}^3$ and are well suited for predicting molecular properties.

Molecular Property Prediction Property Prediction

Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks

1 code implementation • 4 Jul 2020 • Tess E. Smidt, Mario Geiger, Benjamin Kurt Miller

Curie's principle states that "when effects show certain asymmetry, this asymmetry must be found in the causes that gave rise to them".