Litcius/Paper detail

One-component order parameter in URu <sub>2</sub> Si <sub>2</sub> uncovered by resonant ultrasound spectroscopy and machine learning

Sayak Ghosh, Michael Matty, Ryan Baumbach, Eric D. Bauer, K. A. Modic, Arkady Shekhter, J. A. Mydosh, Eun-Ah Kim, B. J. Ramshaw

2020Science Advances42 citationsDOIOpen Access PDF

Abstract

. We observe no anomalies in the shear elastic moduli, providing strong thermodynamic evidence for a one-component order parameter. We develop a machine learning framework that reaches this conclusion directly from the raw data, even in a crystal that is too small for traditional resonant ultrasound. Our result rules out a broad class of theories of hidden order based on two-component order parameters, and constrains the nature of the fluctuations from which unconventional superconductivity emerges at lower temperature. Our machine learning framework is a powerful new tool for classifying the ubiquitous competing orders in correlated electron systems.

Topics & Concepts

Resonant ultrasound spectroscopyCurse of dimensionalityArtificial intelligenceOrder (exchange)SpectroscopyComputer sciencePhysicsMeasure (data warehouse)Machine learningCondensed matter physicsSuperconductivityClass (philosophy)Statistical physicsCrystal (programming language)Shear (geology)State (computer science)Component (thermodynamics)Dimensionality reductionTheoretical physicsTopology (electrical circuits)ElectronRare-earth and actinide compoundsNuclear Materials and PropertiesTopological Materials and Phenomena