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Extraction of interaction parameters for α−RuCl3 from neutron data using machine learning

Anjana M. Samarakoon1,2,3,*, Pontus Laurell4,5,6, Christian Balz2,7, Arnab Banerjee2,8, Paula Lampen-Kelley9,10, David Mandrus9,10, Stephen E. Nagler2,11, Satoshi Okamoto10,11, and D. Alan Tennant1,6,10,11

  • 1Shull-Wollan Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
  • 2Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
  • 3Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, USA
  • 4Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
  • 5Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
  • 6Department of Physics and Astronomy, University of Tennessee, Knoxville, Tennessee 37996, USA
  • 7ISIS Neutron and Muon Source, Rutherford Appleton Laboratory, Didcot, OX11 0QX, United Kingdom
  • 8Department of Physics and Astronomy, Purdue University, West Lafayette, Indiana 47906, USA
  • 9Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA
  • 10Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
  • 11Quantum Science Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA

  • *asamarakoon@anl.gov.

A perspective on machine learning and data science for strongly correlated electron problems

Steven Johnston, Ehsan Khatami, and Richard Scalettar

Carbon Trends 9 100231 (2022)

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