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20172023
most citedE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials

1.9k citations · 3.4k across the 26 of their papers we have counts for

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Showing 2022Show all

2 papers · 1 filter

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…

physics.chem-ph2022★ 5 cited

Cracking the Quantum Scaling Limit with Machine Learned Electron Densities

Joshua A. Rackers, Lucas Tecot, Mario Geiger +1

A long-standing goal of science is to accurately solve the Schrödinger equation for large molecular systems. The poor scaling of current quantum chemistry algorithms on classical c…