Full spin modeling and efficient mapping of the high dimensional Hamiltonian in Dy2Ti2O7

Precise modeling of a material is a key to understand its underlying interactions and physics but also revealing the competing phases in the nearby interaction space. Highly frustrated systems are important due to the richness in physics and diversity of phases including spin liquids with exotic topological states they display. Here, we present a machine learning workflow to fit multi-experimental datasets to find an optimal Hamiltonian while undertaking phase classification and extracting information about the topography around the region of interest. Experimental data from a spin-ice material, Dy₂Ti₂O₇ including diffuse neutron scattering, heat capacity, and susceptibility are utilized. This approach is shown to provide the best model in an efficient and effective way but also is powerful at planning the best experimental strategies.

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Bulletin of the American Physical Society

APS March Meeting 2019

Volume 64, Number 2

Monday–Friday, March 4–8, 2019; Boston, Massachusetts

Session E21: Advances in Computational Methods for Statistical Physics and Their Applications I

Abstract: E21.00004 : Full spin modeling and efficient mapping of the high dimensional Hamiltonian in Dy2Ti2O7

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Abstract: E21.00004 : Full spin modeling and efficient mapping of the high dimensional Hamiltonian in Dy₂Ti₂O₇