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

Upcoming Travel and (Remote) Talks

• [REMOTE] MIT EECS Seminar, February 25, 2021.
• [REMOTE] APS March Meeting Session - C21: Machine Learning for Quantum Matter III, March 15, 2021.
• [REMOTE] Cal State Los Angeles Physics Colloqium, March 25, 2021.
• [REMOTE] CMU AI in Physics Planning Institute Semianr, March 31, 2021.

Code

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• 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

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• Machine Learning on Neutron and X-ray Scattering

  Zhantao Chen, Nina Andrejevic, Nathan Drucker, Thanh Nguyen, R Patrick Xian, Tess Smidt, Yao Wang, Ralph Ernstorfer, Alan Tennant, Maria Chan, Mingda Li, arXiv (2021). arXiv:2102.03024

• 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, Accepted to Advanced Science (2021). arXiv:2009.05163

• SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials

  Simon Batzner, Tess E. Smidt, Lixin Sun, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Boris Kozinsky, arXiv (2021). arXiv:2101.03164

• Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks

  Tess E. Smidt, Mario Geiger, and Benjamin Kurt Miller, Physical Review Research (2021). arXiv:2007.02005
  accompanying code repository

• Euclidean Symmetry and Equivariance in Machine Learning

  Tess E. Smidt, Trends in Chemistry (2021). DOI:10.1016/j.trechm.2020.10.006 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

• 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