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Extraction of interaction parameters for $α − {RuCl}_{3}$ from neutron data using machine learning

Anjana M. Samarakoon ^1,2,3,*, Pontus Laurell ^4,5,6, Christian Balz^2,7, Arnab Banerjee^2,8, Paula Lampen-Kelley^9,10, David Mandrus^9,10, Stephen E. Nagler ^2,11, Satoshi Okamoto^10,11, and D. Alan Tennant^1,6,10,11

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.

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Articles Citing

Extraction of interaction parameters for $α − {RuCl}_{3}$ from neutron data using machine learning

The importance of direct exchange in Kitaev magnetism

Hamiltonian learning quantum magnets with dynamical impurity tomography

Perspectives of Machine Learning for Ligand-Field Analyses in Lanthanide-Based Single Molecule Magnets

Lessons from α -RuCl3 for pursuing quantum spin liquid physics in atomically thin materials

Magnons in van der Waals Antiferromagnetic Materials

Human-machine collaboration: Ordering mechanism of rank-2 spin liquid on breathing pyrochlore lattice

Determination of ligand field parameters of single-molecule magnets from magnetic susceptibility using deep learning based inverse models

Rethinking α−RuCl3 : Parameters, models, and phase diagram

Pressure-dependent magnetism of the Kitaev candidate Li2RhO3

Criticality and Magnetic Phases of Ising Shastry–Sutherland Candidate Holmium Tetraboride

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Pairing tendencies in the doped Kitaev-Heisenberg model

Uncovering obscured phonon dynamics from powder inelastic neutron scattering using machine learning

Update of HΦ: Newly added functions and methods in versions 2 and 3

On ultrafast x-ray scattering methods for magnetism

Application and prospect of machine learning in photoelectron spectroscopy

Static and dynamical magnetic properties of the extended Kitaev-Heisenberg model with spin vacancies

Dynamics of fractionalized mean-field theories: Consequences for Kitaev materials

Simulations of frustrated Ising Hamiltonians using quantum approximate optimization

NaRuO2: Kitaev-Heisenberg exchange in triangular-lattice setting

Capturing dynamical correlations using implicit neural representations

Direct prediction of inelastic neutron scattering spectra from the crystal structure*

Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry

Thermal spin dynamics of Kitaev magnets: Scattering continua and magnetic field induced phases within a stochastic semiclassical approach

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