E3nn: E(3) Equivariant Neural Networks

E3nn.jl provides a pure julia implementation of the e3nn framework. It aims to provide fast and extendable APIs for working with $\mathbb{E}^3$ Equivariant Neural Networks and other performing related operations. It is built on top of GraphNeuralNetworks.jl and Flux.jl. The APIs are generally consistent with e3nn's PyTorch and JAX libraries but there might be certain differences.

Installation

The package isn't available through the Julia Package Manager (yet). If you want to use it, you can add it directly from GitHub.

julia> using Pkg

julia> Pkg.add("https://github.com/Dsantra92/e3nn.jl.git")

Note

The package is still in under early stages of development and the APIs might change in the future.

Getting started

We highly recommend starting with Introduction to Irreps to gain some understanding of the primary components of the network. If you are looking for a bit more hands on experience, have a look at the examples.