e3nn: a modular PyTorch framework for Euclidean neural networks
Welcome!e3nn
!This is the website for the e3nn
repository
https://github.com/e3nn/e3nn/
Documentation
E(3) is the Euclidean group in dimension 3. That is the group of rotations, translations and mirror.
e3nn
is a pytorch library that aims to create E(3) equivariant neural networks.
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.
If you want to talk to other folks using e3nn
, you are most welcome to join our recurring monthly meetings which are shown in the e3nn calendar and our Slack.
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 (tess@e3nn.org
).
Be careful to install a version of torch_geometric
that matches the same CUDA version that your torch
installation uses (this is general advice and not e3nn
specific)
The full instructions for installing e3nn
can be found here.
The documentation is here
Coming soon
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:
We are currently in the progress of compiling a more user-friendly introduction to the core concepts used in e3nn
.
Some previous recorded talks on e3nn
.
2020/11 Euclidean Neural Networks for Physics ∩ ML, November 18, 2020. (slides // video)
2020/09 Unintended Features of E(3)NNs, Workshop on Equivariance and Data Augmentation (video // slides), University of Pennsylvania, September 4, 2020
2020/09 Lecture on Symmetry and Equivariance in ML, Berkeley Lab Deep Learning School (video // slides), Berkeley Lab, September 3, 2020.
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
2019/12 Euclidean Neural Networks for Emulating Ab Initio Calculations and Generating Atomic Geometries, eScience Institute Seminar (video), University of Washington, Seattle, WA
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
A poster overview of the e3nn
framework.
(in reserve chronological order)
e3nn
is used to predict phonon density of states (DOS) from crystal structure. Trained network is used to identify materials with high specific heat.e3nn
on QM9.e3nn
to 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 tess@e3nn.org
with a citation and description that matches the examples above.
The 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 tess@e3nn.org
.
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 support@e3nn.org
to introduce yourself if you are new to joining the meetings.
To get help with a question or code bug, please follow this link.
To get involved with the development and improvement of e3nn
, please follow this link.
e3nn
Team(aka the people answering pull-requests)
e3nn
’s BDFL, mario@e3nn.org
)tess@e3nn.org
)
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