116 citations · 121 across the 3 of their papers we have counts for
3 papers
cond-mat.dis-nn2024★ 3 cited
Phonon predictions with E(3)-equivariant graph neural networks
Shiang Fang, Mario Geiger, Joseph G. Checkelsky +1
We present an equivariant neural network for predicting vibrational and phonon modes of molecules and periodic crystals, respectively. These predictions are made by evaluating the…
stat.ML2023★ 2 cited
A General Framework for Equivariant Neural Networks on Reductive Lie Groups
Ilyes Batatia, Mario Geiger, Jose Munoz +3
Reductive Lie Groups, such as the orthogonal groups, the Lorentz group, or the unitary groups, play essential roles across scientific fields as diverse as high energy physics, quan…
cs.LG2022★ 116 cited
e3nn: Euclidean Neural Networks
Mario Geiger, Tess Smidt
We present e3nn, a generalized framework for creating E(3) equivariant trainable functions, also known as Euclidean neural networks. e3nn naturally operates on geometry and geometr…