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  • Letter
  • Open Access

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.
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Phys. Rev. Research 4, L022061 – Published 21 June, 2022

DOI: https://doi.org/10.1103/PhysRevResearch.4.L022061

Abstract

Single-crystal inelastic neutron-scattering (INS) data contain rich information about the structure and dynamics of a material. Yet the challenge of matching sophisticated theoretical models with large data volumes is compounded by computational complexity and the ill-posed nature of the inverse scattering problem. Here we utilize a novel machine-learning (ML)-assisted framework featuring multiple neural network architectures to address this via high-dimensional modeling and numerical methods. A comprehensive data set of diffraction and INS measured on the Kitaev material α−RuCl3 is processed to extract its Hamiltonian. Semiclassical Landau-Lifshitz dynamics and Monte-Carlo simulations were employed to explore the parameter space of an extended Kitaev-Heisenberg Hamiltonian. A ML-assisted iterative algorithm was developed to map the uncertainty manifold to match experimental data, a nonlinear autoencoder was used to undertake information compression, and radial basis networks were utilized as fast surrogates for diffraction and dynamics simulations to predict potential spin Hamiltonians with uncertainty. Exact diagonalization calculations were employed to assess the impact of quantum fluctuations on the selected parameters around the best prediction.

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Phys. Rev. Research 4, L022061– Published 21 June, 2022
  • Received 23 February 2022
  • Accepted 4 May 2022
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