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 se3cnn
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 se3cnn
. This tutorial was originally presented at IPAM at UCLA on November 14, 2019.
Convolutional neural networks for point clouds and voxels with Euclidean symmetries (3D translation and 3D rotation). The current code (on the point
branch) is a merger of the work from Tensor field networks and 3D Steerable CNNs.
code: paper repo / recent – se3cnn
code:
pymatgen.analysis.ferroelectricity /
atomate…workflow…ferroelectric
data:
ferroelectric_search_site
figshare data deposit