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

I will be starting as an Assistant Professor of EECS at MIT in Fall 2021.

Upcoming Travel and (Remote) Talks

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Code

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• 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. See e3nn.org and docs.e3nn.org for more info.

Recent pre-prints and publications

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• SE(3)-equivariant prediction of molecular wavefunctions and electronic densities

  Oliver T. Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess Smidt, Klaus-Robert Müller (2021). arXiv:2106.02347

• Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning

  Alice Gatti, Zhixiong Hu, Tess Smidt, Esmond G. Ng, Pieter Ghysels (2021). arXiv:2104.03546

• 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, 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