Machine Learning for Quantum Matter V
FOCUS · L21 · ID: 381558
Presentations
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Closed-loop discovery of optimal materials using artificial intelligence
Invited
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Presenters
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Muratahan Aykol
- Toyota Research Institute
- Energy Technologies Area, Lawrence Berkeley National Laboratory
Authors
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Muratahan Aykol
- Toyota Research Institute
- Energy Technologies Area, Lawrence Berkeley National Laboratory
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Interpretable and unsupervised phase classification based on averaged input features
ORAL
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Presenters
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Julian Arnold
- Department of Physics, University of Basel
Authors
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Julian Arnold
- Department of Physics, University of Basel
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Frank Schäfer
- Department of Physics, University of Basel
- University of Basel
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Martin Zonda
- Institute of Physics, Albert-Ludwigs-Universität Freiburg
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Axel U. J. Lode
- University of Freiburg
- Institute of Physics, Albert-Ludwig University of Freiburg
- Institute of Physics, Albert-Ludwigs-Universität Freiburg
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Exploration of Topological Metamaterial Band Structures and Chern numbers using Deep Learning
ORAL
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Presenters
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Vittorio Peano
- Max Planck Institute for the Science of Light
- Max Planck Inst for Sci Light
Authors
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Vittorio Peano
- Max Planck Institute for the Science of Light
- Max Planck Inst for Sci Light
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Florian Sapper
- Max Planck Inst for Sci Light
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Florian Marquardt
- Univ Erlangen Nuremberg
- Max Planck Inst for Sci Light
- Max Planck Institute for the Science of Light
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Unsupervised learning of topological order
ORAL
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Presenters
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Gebremedhin Dagnew
- Middlebury College
Authors
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Gebremedhin Dagnew
- Middlebury College
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Owen Myers
- Hometap
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Chris M Herdman
- Middlebury College
- Physics, Middlebury College
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Lauren Haywards
- Perimeter Institute for Theoretical Physics
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Machine learning augmented neutron and x-ray scattering for quantum materials
Invited
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Presenters
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Mingda Li
- Nuclear Science and Engineering, Massachusetts Institute of Technology
- Massachusetts Institute of Technology
- Massachusetts Institute of Technology MIT
Authors
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Mingda Li
- Nuclear Science and Engineering, Massachusetts Institute of Technology
- Massachusetts Institute of Technology
- Massachusetts Institute of Technology MIT
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Topological quantum phase transitions retrieved through unsupervised machine learning
ORAL
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Presenters
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Yanming Che
- RIKEN
Authors
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Yanming Che
- RIKEN
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Clemens Gneiting
- RIKEN, Japan
- RIKEN
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Tao Liu
- RIKEN
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Franco Nori
- RIKEN, Japan and Univ. Michigan, USA
- RIKEN, Japan
- RIKEN; and Univ. Michigan.
- RIKEN, Japan; and Univ. Michigan, USA
- Riken Japan and Univ. Michigan USA
- RIKEN, Japan and Univ Michigan, USA
- Theoretical Quantum Physics Laboratory, Department of Physics, RIKEN Cluster for Pioneering Research, The University of Michigan
- RIKEN and Univ. of Michigan
- Riken Japan and Univ Michigan USA
- RIKEN; and University of Michigan
- RIKEN and Univ. Michigan
- RIKEN and Univ of Michigan
- Theoretical Quantum Physics Laboratory, RIKEN Cluster for Pioneering Research, Wako-shi, Saitama 351-0198, Japan
- RIKEN, and University of Michigan
- Theoretical Quantum Physics, Riken, Japan
- RIKEN, Japan; and Univ Michigan, USA
- Theoretical Quantum Physics Laboratory, RIKEN
- RIKEN, Japan; Univ. Michigan, USA
- RIKEN, Japan; Uni. Michigan, USA
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Machine learning dynamics of phase separation in correlated electron magnets
ORAL
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Presenters
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Puhan Zhang
- Univ of Virginia
Authors
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Puhan Zhang
- Univ of Virginia
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Preetha Saha
- Univ of Virginia
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Gia-Wei Chern
- Univ of Virginia
- University of Virginia
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Machine learning spectral indicators of topology
ORAL
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Presenters
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Nina Andrejevic
- Nuclear Science and Engineering, Massachusetts Institute of Technology
- Massachusetts Institute of Technology
Authors
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Nina Andrejevic
- Nuclear Science and Engineering, Massachusetts Institute of Technology
- Massachusetts Institute of Technology
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Jovana Andrejevic
- Harvard University
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Christopher Rycroft
- Harvard University
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Mingda Li
- Nuclear Science and Engineering, Massachusetts Institute of Technology
- Massachusetts Institute of Technology
- Massachusetts Institute of Technology MIT
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AI-guided engineering of nanoscale topological materials
ORAL
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Presenters
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Srilok Srinivasan
- Argonne National Laboratory
- Center for Nanoscale Materials, Argonne National Laboratory
Authors
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Srilok Srinivasan
- Argonne National Laboratory
- Center for Nanoscale Materials, Argonne National Laboratory
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Mathew Cherukara
- Argonne National Laboratory
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David Eckstein
- Argonne National Laboratory
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Anthony Avarca
- Argonne National Laboratory
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Subramanian Sankaranarayanan
- Argonne National Laboratory
- Center for Nanoscale Materials, Argonne National Laboratory
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Pierre Darancet
- Argonne National Laboratory
- Center for Nanoscale Materials, Argonne National Laboratory
- Center for Nanoscale Materials, Argonne National Lab
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Automatic Learning of Topological Phase Boundaries
ORAL
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Presenters
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Alexander Kerr
- Center for Quantum Research and Technology, Univ of Oklahoma
Authors
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Alexander Kerr
- Center for Quantum Research and Technology, Univ of Oklahoma
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Geo Jose
- Univ of Oklahoma
- Center for Quantum Research and Technology, Univ of Oklahoma
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Colin J Riggert
- Center for Quantum Research and Technology, Univ of Oklahoma
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Kieran Mullen
- Center for Quantum Research and Technology, Univ of Oklahoma
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