Luis W. Alvarez Postdoctoral Fellow in Computing Sciences, Berkeley Lab
Atomic Architects Research Website
This tutorial uses a combination of slides and interactive jupyter notebooks using the e3nn
framework to present fundamental concepts about Euclidean equivariant neural networks: data types, equivariant operations, how symmetry effects simple tasks, and how to construct the building blocks of e3nn
. This tutorial was originally presented at IPAM at UCLA on November 14, 2019.
e3nn
: A framework for Euclidean symmetry equivariant neural networksConvolutional neural networks for point clouds and voxels with Euclidean symmetries (3D translation and 3D rotation). The current code is a merger of the work from Tensor field networks and 3D Steerable CNNs.
Tess E. Smidt, Mario Geiger, and Benjamin Kurt Miller (arXiv:2007.02005)
code: (notebook / html)
Maserati, L. et al, Materials Horizons, 2020, DOI: 10.1039/C9MH01917K
Smidt, T.E., Mack, S.A., Reyes-Lillo, S.E. et al. Sci Data 7, 72 (2020).
code:
pymatgen.analysis.ferroelectricity /
atomate…workflow…ferroelectric
data:
ferroelectric_search_site
figshare data deposit
code: paper repo / recent – e3nn