Gemnet: Universal directional graph neural networks for molecules
J Gasteiger, F Becker… - Advances in Neural …, 2021 - proceedings.neurips.cc
Effectively predicting molecular interactions has the potential to accelerate molecular
dynamics by multiple orders of magnitude and thus revolutionize chemical simulations …
dynamics by multiple orders of magnitude and thus revolutionize chemical simulations …
Theory for equivariant quantum neural networks
Quantum neural network architectures that have little to no inductive biases are known to
face trainability and generalization issues. Inspired by a similar problem, recent …
face trainability and generalization issues. Inspired by a similar problem, recent …
On the expressive power of geometric graph neural networks
The expressive power of Graph Neural Networks (GNNs) has been studied extensively
through the Weisfeiler-Leman (WL) graph isomorphism test. However, standard GNNs and …
through the Weisfeiler-Leman (WL) graph isomorphism test. However, standard GNNs and …
Equivariance with learned canonicalization functions
Symmetry-based neural networks often constrain the architecture in order to achieve
invariance or equivariance to a group of transformations. In this paper, we propose an …
invariance or equivariance to a group of transformations. In this paper, we propose an …
An efficient Lorentz equivariant graph neural network for jet tagging
A bstract Deep learning methods have been increasingly adopted to study jets in particle
physics. Since symmetry-preserving behavior has been shown to be an important factor for …
physics. Since symmetry-preserving behavior has been shown to be an important factor for …
Sign and basis invariant networks for spectral graph representation learning
We introduce SignNet and BasisNet--new neural architectures that are invariant to two key
symmetries displayed by eigenvectors:(i) sign flips, since if $ v $ is an eigenvector then so is …
symmetries displayed by eigenvectors:(i) sign flips, since if $ v $ is an eigenvector then so is …
Gemnet: Universal directional graph neural networks for molecules
J Klicpera, F Becker, S Günnemann - Proceedings of the 35th …, 2021 - dl.acm.org
Effectively predicting molecular interactions has the potential to accelerate molecular
dynamics by multiple orders of magnitude and thus revolutionize chemical simulations …
dynamics by multiple orders of magnitude and thus revolutionize chemical simulations …
Geometrically equivariant graph neural networks: A survey
Many scientific problems require to process data in the form of geometric graphs. Unlike
generic graph data, geometric graphs exhibit symmetries of translations, rotations, and/or …
generic graph data, geometric graphs exhibit symmetries of translations, rotations, and/or …
Physics-guided deep learning for dynamical systems: A survey
Modeling complex physical dynamics is a fundamental task in science and engineering.
Traditional physics-based models are sample efficient, and interpretable but often rely on …
Traditional physics-based models are sample efficient, and interpretable but often rely on …