Machine Learning for Quantum Matter III
FOCUS · C21 · ID: 381557
Presentations
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Neural networks for atomistic modelling - are we there yet?
Invited
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Presenters
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Emine Kucukbenli
- Harvard University
Authors
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Emine Kucukbenli
- Harvard University
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Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks
ORAL
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Presenters
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Tess Smidt
- Lawrence Berkeley National Laboratory
- Computational Research Division, Lawrence Berkeley National Laboratory
Authors
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Tess Smidt
- Lawrence Berkeley National Laboratory
- Computational Research Division, Lawrence Berkeley National Laboratory
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Mario Geiger
- École polytechnique fédérale de Lausanne
- Ecole Polytechnique Federale de Lausanne
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Benjamin Kurt Miller
- University of Amsterdam
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Machine learning dielectric screening for the simulation of excited state properties of molecules and materials
ORAL
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Presenters
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Sijia Dong
- Argonne National Laboratory
- Materials Science Division, Argonne National Laboratory
Authors
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Sijia Dong
- Argonne National Laboratory
- Materials Science Division, Argonne National Laboratory
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Marco Govoni
- Materials Science Division and Center for Molecular Engineering, Argonne National Laboratory
- Argonne National Laboratory
- Materials Science Division, Argonne National Laboratory
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Giulia Galli
- The University of Chicago
- Pritzker School of Molecular Engineering, The University of Chicago
- Pritzker School of Molecular Engineering, University of Chicago
- University of Chicago
- Department of Chemistry, University of Chicago
- Materials Science Division and Center for Molecular Engineering, Argonne National Laboratory
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Generative Model Learning For Molecular Electronics
ORAL
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Presenters
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Andrew Mitchell
- Univ Coll Dublin
- Physics, University College Dublin
Authors
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Andrew Mitchell
- Univ Coll Dublin
- Physics, University College Dublin
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Jonas Rigo
- Physics, University College Dublin
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Sudeshna Sen
- Physics, University College Dublin
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An assessment of the structural resolution of various fingerprints commonly used in machine learning
ORAL
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Presenters
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Behnam Parsaeifard
- University of Basel
Authors
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Behnam Parsaeifard
- University of Basel
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Deb De
- University of Basel
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Anders Christensen
- University of Basel
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Felix A Faber
- University of Basel
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Emir Kocer
- goettingen university
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Sandip De
- University of Basel
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Jorg Behler
- Theoretische Chemie, Georg-August-Universität Göttingen
- goettingen university
- University of Göttingen
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O. Von Lilienfeld
- University of Basel
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Stefan A Goedecker
- Physics, University of Basel
- University of Basel
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Vestigial nematic order in Pd-RTe3 studied using X-ray diffraction TEmperature Clustering (X-TEC)
ORAL
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Presenters
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Eun-Ah Kim
- Cornell University
- Department of Physics, Cornell University
Authors
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Krishnanand Mallayya
- Cornell University
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Michael Matty
- Cornell University
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Joshua Straquadine
- Stanford University
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Matthew Krogstad
- Materials Science Division, Argonne National Laboratory
- Argonne National Laboratory
- Materials Science Division, Argonne National Lab
- Material Science, Argonne National Laboratory
- Material Science Division, Argonne National Laboratory
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Raymond Osborn
- Materials Science Division, Argonne National Laboratory
- Argonne National Laboratory
- Materials Science Division, Argonne National Lab
- Materials Science, Argonne National Laboratory
- Material Science, Argonne National Laboratory
- Material Science Division, Argonne National Laboratory
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Stephan Rosenkranz
- Materials Science Division, Argonne National Laboratory
- Argonne National Laboratory
- Materials Science Division, Argonne National Lab
- Materials Science, Argonne National Laboratory
- Material Science, Argonne National Laboratory
- Material Science Division, Argonne National Laboratory
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Ian R Fisher
- Geballe Laboratory for Advanced Materials, Stanford University
- Stanford Univ
- Stanford University
- Department of Applied Physics, Stanford University
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Eun-Ah Kim
- Cornell University
- Department of Physics, Cornell University
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Reactive Machine Learning Potential Models for the NO Formation Reaction
ORAL
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Presenters
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Andrew Johannesen
- University of Minnesota
Authors
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Andrew Johannesen
- University of Minnesota
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Jason Goodpaster
- University of Minnesota
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Achieving Smaller Effective Spot Sizes in nano-ARPES with Machine Learning
ORAL
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Presenters
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Conrad Stansbury
- University of California, Berkeley
Authors
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Conrad Stansbury
- University of California, Berkeley
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Alessandra Lanzara
- University of California, Berkeley
- Department of Physics, University of California
- Physics, University of California, Berkeley
- Lawrence Berkeley National Laboratory
- Department of Physics, University of California Berkeley
- Physics, University of California Berkeley
- Physics, UC Berkeley
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INVESTIGATING BAND GAP DIRECTNESS USING MACHINE LEARNING
ORAL
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Presenters
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Elton Ogoshi de Melo
- Center for Natural and Human Sciences, Federal University of ABC
Authors
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Elton Ogoshi de Melo
- Center for Natural and Human Sciences, Federal University of ABC
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Mário Popolin Neto
- Institute of Mathematics and Computer Sciences, University of São Paulo
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Carlos Mera Acosta
- Univ Federal do ABC
- Renewable and Sustainable Energy Institute, University of Colorado, Boulder, Colorado 80309, USA
- RASEI, University of Colorado, Boulder
- Center for Natural and Human Sciences, Federal University of ABC
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Gabriel M. Nascimento
- Center for Natural and Human Sciences, Federal University of ABC
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João Rodrigues
- Center for Natural and Human Sciences, Federal University of ABC
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Osvaldo N. Oliveira Jr.
- São Carlos Institute of Physics, University of São Paulo
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Fernando V. Paulovich
- Faculty of Computer Science, Dalhousie University
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Gustavo M. Dalpian
- Center for Natural and Human Sciences, Federal University of ABC
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Unsupervised machine learning of quantum phase transitions using diffusion maps
ORAL
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Presenters
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Alex Lidiak
- Colorado School of Mines
Authors
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Alex Lidiak
- Colorado School of Mines
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