T
Tess Smidt
Researcher at Lawrence Berkeley National Laboratory
Publications - 44
Citations - 2574
Tess Smidt is an academic researcher from Lawrence Berkeley National Laboratory. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 14, co-authored 35 publications receiving 1414 citations. Previous affiliations of Tess Smidt include Massachusetts Institute of Technology & University of California, Berkeley.
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Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds
Nathaniel Cabot Thomas,Tess Smidt,Steven Kearnes,Lusann Yang,Li Li,Kai Kohlhoff,Patrick Riley +6 more
TL;DR: Tensor field neural networks are introduced, which are locally equivariant to 3D rotations, translations, and permutations of points at every layer, and demonstrate the capabilities of tensor field networks with tasks in geometry, physics, and chemistry.
Journal ArticleDOI
Design and Construction of the MicroBooNE Detector
R. Acciarri,C. Adams,R. An,A. Aparicio,S. Aponte,J. Asaadi,M. Auger,N. Ayoub,L. Bagby,B. Baller,R. Barger,G.D. Barr,M. Bass,F. Bay,K. Biery,M. Bishai,Andrew Blake,V. Bocean,D. J. Boehnlein,V. D. Bogert,T. Bolton,L. Bugel,C. Callahan,L. Camilleri,D. Caratelli,B. Carls,R. Castillo Fernandez,F. Cavanna,S. Chappa,H. S. Chen,Kai Chen,C.-Y. Chi,Christie S. Chiu,E. Church,D. Cianci,D. Cianci,G. H. Collin,Janet Conrad,M. E. Convery,J. Cornele,P. Cowan,J. I. Crespo-Anadón,G. Crutcher,Christine Darve,R. Davis,M. Del Tutto,D. Devitt,S. Duffin,S. Dytman,B. Eberly,Antonio Ereditato,D. Erickson,L. Escudero Sanchez,J. Esquivel,S. Farooq,J. Farrell,D. Featherston,B.T. Fleming,W. Foreman,A. P. Furmanski,V. Genty,M. Geynisman,D. Goeldi,B. Goff,S. Gollapinni,N. Graf,E. Gramellini,J. Green,A. Greene,H. Greenlee,T. Griffin,R. Grosso,R. Guenette,A. Hackenburg,R. Haenni,P. M. Hamilton,P. Healey,Or Hen,E. Henderson,J. Hewes,Colin Hill,K. Hill,L. Himes,J. Ho,G. A. Horton-Smith,D. Huffman,C. M. Ignarra,C. James,E. James,J. Jan de Vries,W. Jaskierny,C.-M. Jen,L. Jiang,B. Johnson,Marvin Johnson,R. A. Johnson,B. J. P. Jones,J. Joshi,H. Jöstlein,D. Kaleko,L. N. Kalousis,G. Karagiorgi,G. Karagiorgi,Teppei Katori,P. Kellogg,W. Ketchum,J. Kilmer,Barry King,B. Kirby,Michael H Kirby,E. Klein,T. Kobilarcik,I. Kreslo,R. Krull,Robert M. Kubinski,G. Lange,Francesco Lanni,A. Lathrop,A. Laube,W. M. Lee,Yang Li,D. Lissauer,A. Lister,B. R. Littlejohn,S. Lockwitz,D. Lorca,W. C. Louis,Gennadiy Lukhanin,M. Luethi,B. Lundberg,X. Luo,G. Mahler,I. Majoros,D. Makowiecki,A. Marchionni,C. Mariani,D. Markley,John Marshall,D. A. Martinez Caicedo,Kirk T. McDonald,D. McKee,A.I.L. McLean,Joseph Mead,V. Meddage,T. Miceli,G. B. Mills,W. Miner,J. Moon,M. Mooney,C.D. Moore,Z. Moss,J. Mousseau,R. Murrells,D. Naples,P. Nienaber,B. Norris,N. Norton,J. A. Nowak,M. O'Boyle,T. Olszanowski,Ornella Palamara,V. Paolone,V. Papavassiliou,S. F. Pate,Z. Pavlovic,R. Pelkey,M. Phipps,S. Pordes,D. Porzio,G. Pulliam,Xin Qian,J. L. Raaf,Veljko Radeka,A. Rafique,R. A. Rameika,B. Rebel,R. Rechenmacher,S. Rescia,L. Rochester,C. Rudolf von Rohr,A. Ruga,B. Russell,R. Sanders,W. Sands,M. Sarychev,D. W. Schmitz,A. Schukraft,R. Scott,W. G. Seligman,M. H. Shaevitz,M. Shoun,J. Sinclair,W. Sippach,Tess Smidt,A.D. Smith,E.L. Snider,M. Soderberg,M. Solano-Gonzalez,S. Söldner-Rembold,S.R. Soleti,J. Sondericker,Panagiotis Spentzouris,J. Spitz,J. St. John,Thomas Strauss,K. Sutton,A. M. Szelc,K. Taheri,N. Tagg,K. Tatum,J. Teng,Kazuhiro Terao,M. A. Thomson,C. E. Thorn,J. Tillman,M. Toups,Y.-T. Tsai,S. Tufanli,T. Usher,M. Utes,R. G. Van de Water,C. Vendetta,S. Vergani,E. Voirin,J. Voirin,B. Viren,P. Watkins,Marc Weber,T. Wester,Jason Weston,D.A. Wickremasinghe,S. Wolbers,T. Wongjirad,K. Woodruff,K. C. Wu,T. Yang,Bo Yu,G. P. Zeller,J. Zennamo,Chao Zhang,M. Zuckerbrot +240 more
TL;DR: MicroBooNE as discussed by the authors is the first phase of the Short Baseline Neutrino program, located at Fermilab, and will utilize the capabilities of liquid argon detectors to examine a rich assortment of physics topics.
Posted ContentDOI
SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Simon Batzner,Tess Smidt,Lixin Sun,Jonathan P. Mailoa,Mordechai Kornbluth,Nicola Molinari,Boris Kozinsky +6 more
TL;DR: The NequIP method achieves state-of-the-art accuracy on a challenging set of diverse molecules and materials while exhibiting remarkable data efficiency, challenging the widely held belief that deep neural networks require massive training sets.
Journal ArticleDOI
Realization of a three-dimensional spin–anisotropic harmonic honeycomb iridate
Kimberly Modic,Tess Smidt,Itamar Kimchi,Nicholas Breznay,Alun Biffin,Sungkyun Choi,Roger Johnson,Radu Coldea,Pilanda Watkins-Curry,Gregory T. McCandless,Julia Y. Chan,Felipe Gándara,Zahirul Islam,Ashvin Vishwanath,Arkady Shekhter,Ross D. McDonald,James Analytis +16 more
TL;DR: A new iridate structure that has the same local connectivity as the layered honeycomb and exhibits striking evidence for highly spin-anisotropic exchange is reported, suggesting a new family of three-dimensional structures could exist, the 'harmonic honeycomb' iridates.
Journal ArticleDOI
Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows
Kiran Mathew,Kiran Mathew,Joseph Montoya,Alireza Faghaninia,Shyam Dwarakanath,Muratahan Aykol,Hanmei Tang,Iek-Heng Chu,Tess Smidt,Tess Smidt,Brandon Bocklund,Matthew Horton,John Dagdelen,Brandon Wood,Zi Kui Liu,Jeffrey B. Neaton,Jeffrey B. Neaton,Shyue Ping Ong,Kristin A. Persson,Kristin A. Persson,Anubhav Jain +20 more
TL;DR: An open-source Python framework for computational materials science simulation, analysis, and design with an emphasis on automation and extensibility, atomate provides both fully functional workflows as well as reusable components that enable one to compose complex materials science workflows that use a diverse set of computational tools.