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Machine Learning Analysis of Structural Order and Goldstone-Mode Fluctuations in Cd<sub>2</sub>Re<sub>2</sub>O<sub>7</sub>

ORAL

Abstract

Spin-orbit coupling in transition metal compounds with 4d and 5d electrons is predicted to generate a wide variety of novel parity-breaking correlated electron phases, but evidence of a concomitant structural response is often lacking. Cd2Re2O7 is a pyrochlore that has been proposed to exhibit multipolar nematic order below two structural phase transitions at 200K and 113K, but the symmetry of the order parameters are under dispute. We report high-energy x-ray scattering measurements of 3D reciprocal space volumes comprising over 10,000 Brillouin zones in a fine Q-grid performed at many temperatures from 300K to 30K. We have analyzed the data with unsupervised machine learning, using the newly-developed X-TEC algorithm [1], to classify the temperature dependence of both superlattice peaks and diffuse scattering. The order parameter below 200K results from cation displacements consistent with the condensation of a nearly-degenerate two-component Eu mode. X-TEC also identified diffuse scattering below 200K from Goldstone mode fluctuations between the two Eu modes, which could explain the lower transition at 113 K.

[1] Venderley et al, https://arxiv.org/abs/2008.03275

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Presenters

  • 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

Authors

  • 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

  • Eun-Ah Kim

    Cornell University, Department of Physics, Cornell University

  • 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

  • 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

  • Jordan M Venderley

    Cornell University

  • Michael Matty

    Cornell University

  • Kilian Q Weinberger

    Cornell University, Department of Computer Science, Cornell University

  • David George Mandrus

    Materials Science and Technology Division, Oak Ridge National Labratory, Materials Science and Engineering, University of Tennessee, Department of Materials Science and Engineering, University of Tennessee, University of Tennessee, Department of Materials Science and Engineering, University of Tennessee Knoxville, Department of Materials Science & Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA, Oakridge National Laboratory, Materials Science and Engineering, University of Tennessee, Knoxville, Oak Ridge National Laboratory, University of Tennessee - Knoxville, Materials Science and Technology Division, Oak Ridge National Laboratory, Department of Physics, University of Tennessee Knoxville, Materials Science and Technology, Oak Ridge National Laboratory, Oak Ridge National Laboratory, Materials Science and Technology Division, Department of Materials Science, The University of Tennessee, University of Tennessee, Knoxville