Atomic architect. Wrangler of neural nets. Geometry junkie. blondegeek.

Upcoming Travel and (Remote) Talks

• Tutorial on e3nn for the Scientific Machine Learning Mini-Course hosted by CMU, November 5, 2020.
• Lecture + Tutorial on e3nn for the Machine learning in Physics, Chemistry and Materials discussion group (MLDG) at the University of Cambridge, November 9, 2020.
• Seminar on Euclidean Neural Networks for Physics ∩ ML, November 18, 2020.

Code

• e3nn_tutorial: Tutorial on Euclidean symmetry equivariant neural networks

  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 networks

  Convolutional 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.

Recent pre-prints and publications

• Direct prediction of phonon density of states with Euclidean neural network

  Zhantao Chen, Nina Andrejevic, Tess Smidt, Zhiwei Ding, Yen-Ting Chi, Quynh T. Nguyen, Ahmet Alatas, Jing Kong, Mingda Li, arXiv (2020). arXiv:2009.05163

• Euclidean Symmetry and Equivariance in Machine Learning

  Tess E. Smidt, ChemRxiv (2020). DOI:10.26434/chemrxiv.12935198

• Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties

  Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé, arXiv (2020). arXiv:2008.08461

• Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks

  Tess E. Smidt, Mario Geiger, and Benjamin Kurt Miller, arXiv (2020). arXiv:2007.02005
  
  code: (notebook / html)

• Anisotropic 2D Excitons Unveiled in Organic-Inorganic Quantum Wells

  Maserati, L. et al, Materials Horizons (2020). DOI:10.1039/C9MH01917K

• An Automatically Curated First-Principles Database of Ferroelectrics

  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

• Tensor Field Networks

  code: paper repo / recent – e3nn