Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

6 October 2021

Papers citing "Geometric and Physical Quantities Improve E(3) Equivariant Message Passing"

7 / 157 papers shown

Title
Boundary Graph Neural Networks for 3D Simulations Andreas Mayr Sebastian Lehner A. Mayrhofer C. Kloss Sepp Hochreiter Johannes Brandstetter AI4CE 22 33 0 21 Jun 2021
Steerable Partial Differential Operators for Equivariant Neural Networks Erik Jenner Maurice Weiler 25 28 0 18 Jun 2021
Vector Neurons: A General Framework for SO(3)-Equivariant Networks Congyue Deng Or Litany Yueqi Duan A. Poulenard Andrea Tagliasacchi Leonidas J. Guibas 3DPC 125 317 0 25 Apr 2021
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups Marc Finzi Max Welling A. Wilson 79 186 0 19 Apr 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon L. Batzner Albert Musaelian Lixin Sun Mario Geiger J. Mailoa M. Kornbluth N. Molinari Tess E. Smidt Boris Kozinsky 254 1,241 0 08 Jan 2021
The Open Catalyst 2020 (OC20) Dataset and Community Challenges L. Chanussot Abhishek Das Siddharth Goyal Thibaut Lavril Muhammed Shuaibi ... Brandon M. Wood Junwoong Yoon Devi Parikh C. L. Zitnick Zachary W. Ulissi 232 505 0 20 Oct 2020
Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey M. Rath A. P. Condurache ViT AI4CE 37 9 0 30 Jun 2020