This is the website for the
If you’d like to generally learn what
e3nn is and what it’s used for, check out some of previously recorded talks and skimming some of the papers. If you’d like to try out
e3nn, the first step is to install it.
Once you have
e3nn up and running, try out the Jupyter notebooks in e3nn_tutorial. This will give you a feel for the primary data types and classes in
e3nn. You can also read e3nn_book in parallel and dive into more rigorous math resources. This reading will be helpful for parsing even the most techincal parts of the relevant papers.
We want to help eliminate any bottlenecks in making Euclidean equivariant neural networks and
e3nn accessible to a broad audience, especially scientists whose primary expertise is not machine learning. If you have an application in mind but not sure how to structure a network around it or other questions not satisfied by this page’s resources, feel free to reach out to Tess (
tsmidt at lbl gov).
e3nn and dependencies if you are only going to use the
cpu is straightforward. Things get a bit more complicated if you want to
GPU. If you want to use
e3nn you need to:
torch_geometricthat matches the same CUDA version that your
torchinstallation uses (this is general advice and not
nvcc) for your CUDA installation so you can compile
e3nn makes use of group theory and representation theory. You don’t need to be knowledgeable on these topics to start using
e3nn but it might be useful to have some relevant resources handy. Some of our favorite resources are:
This link has our quick tutorial and this website / respository has many illustrative notebooks on how to use
e3nn. If you run into an error while running these notebooks – please make a pull request!
We are currently in the progress of compiling a more user-friendly introduction to the core concepts used in
e3nn. You can see the (very incomplete) draft of the book here.
Some previous recorded talks on
2020/07 Neural Networks with Euclidean Symmetry for Physical Sciences, 1st Workshop on Scientific-Driven Deep Learning (SciDL) (video // slides), July 1, 2020
2020/01 An autoencoder for discrete geometry, Applied Machine Learning Days – AI and the Molecular World (video), EPFL, January 27-28, 2020, Lausanne, Switzerland
2019/09 Euclidean Neural Networks for Emulating Ab Initio Calculations and Generating Atomic Geometries, Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics, (video // slides), IPAM at UCLA, Los Angeles, CA
(in reserve chronological order)
e3nnis used to predict phonon density of states (DOS) from crystal structure. Trained network is used to identify materials with high specific heat.
e3nnto perform a symmetry analysis to resolve order parameters for 2nd order phase transitions.
*indicates foundational paper in the development of Euclidean neural networks.
If there are any papers that you think should be on this list but are missing, please email Tess
tsmidt at lbl gov with a citation and description that matches the examples above.
e3nn developers and several collaborators discuss ideas and help each other out with projects via Slack. If you’d like to join the Slack, please send an email to Tess
tsmidt at lbl gov.
e3nn has two recurring monthly meetings in addition to other events on our calendar.
Everyone is welcome to join these meetings – yes, that means you! The meeting links are in the calendar events show below. Feel free to reach out to Tess
tsmidt at lbl gov to introduce yourself if you are new to joining the meetings.
(aka the people answering pull-requests)