arXiv subjects

D. Alan Tennant

Publications and source records attributed to D. Alan Tennant.

28 records · Page 2Linked to original sources

arXiv2021

Integration of Machine Learning with Neutron Scattering: Hamiltonian Tuning in Spin Ice with Pressure

Quantum materials research requires co-design of theory with experiments and involves demanding simulations and the analysis of vast quantities of data, usually including pattern recognition and clustering. Artificial intelligence is a natural route to optimise these processes and bring theory and experiments together. Here we propose a scheme that integrates machine learning with high-performance simulations and scattering measurements, covering the pipeline of typical neutron experiments. Our approach uses nonlinear autoencoders trained on realistic simulations along with a fast surrogate for the calculation of scattering in the form of a generative model. We demonstrate this approach in a highly frustrated magnet, Dy$_2$Ti$_2$O$_7$, using machine learning predictions to guide the neutron scattering experiment under hydrostatic pressure, extract material parameters and construct a phase diagram. Our scheme provides a comprehensive set of capabilities that allows direct integration of theory along with automated data processing and provides on a rapid timescale direct insight into a challenging condensed matter system.

cond-mat.other↗

arXiv2021

Unusual exchange couplings and intermediate temperature Weyl state in Co3Sn2S2

Understanding the magnetism and its possible correlations to the topological properties has emerged as a forefront and difficult topic in studying magnetic Weyl semimetals. Co$_{3}$Sn$_{2}$S$_{2}$ is a newly discovered magnetic Weyl semimetal with a kagome lattice of cobalt ions and has triggered intense interest for rich fantastic phenomena. Here, we report the magnetic exchange couplings of Co$_{3}$Sn$_{2}$S$_{2}$ using inelastic neutron scattering and two density functional theory (DFT) based methods: constrained magnetism and multiple-scattering Green's function methods. Co$_{3}$Sn$_{2}$S$_{2}$ exhibits highly anisotropic magnon dispersions and linewidths below $T_{C}$, and paramagnetic excitations above $T_{C}$. The spin-wave spectra in the ferromagnetic ground state is well described by the dominant third-neighbor "across-hexagon" $J_{d}$ model. Our density functional theory calculations reveal that both the symmetry-allowed 120$^\circ$ antiferromagnetic orders support Weyl points in the intermediate temperature region, with distinct numbers and the locations of Weyl points. Our study highlights the important role Co$_{3}$Sn$_{2}$S$_{2}$ can play in advancing our understanding of kagome physics and exploring the interplay between magnetism and band topology.

cond-mat.str-el↗

arXiv2021

Anomalous magnetic noise in imperfect flat bands in the topological magnet Dy2Ti2O7

The spin ice compound Dy_2Ti_2O_7 stands out as the first topological magnet in three dimensions, with its tell-tale emergent fractionalized magnetic monopole excitations. Its real-time dynamical properties have been an enigma from the very beginning. Using ultrasensitive, non-invasive SQUID measurements, we show that Dy_2Ti_2O_7 exhibits a highly anomalous noise spectrum, in three qualitatively different regimes: equilibrium spin ice, a `frozen' regime extending to ultra-low temperatures, as well as a high-temperature `anomalous' paramagnet. We show that in the simplest model of spin ice, the dynamics is not anomalous, and we present several distinct mechanisms which give rise to a coloured noise spectrum. In addition, we identify the structure of the single-ion dynamics as a crucial ingredient for any modelling. Thus, the dynamics of spin ice Dy_2Ti_2O_7 reflects the interplay of local dynamics with emergent topological degrees of freedom and a frustration-generated imperfectly flat energy landscape, and as such should be relevant for a broad class of magnetic materials.

cond-mat.str-el↗

arXiv2021

Quantifying and controlling entanglement in the quantum magnet Cs$_2$CoCl$_4$

The lack of methods to experimentally detect and quantify entanglement in quantum matter impedes our ability to identify materials hosting highly entangled phases, such as quantum spin liquids. We thus investigate the feasibility of using inelastic neutron scattering (INS) to implement a model-independent measurement protocol for entanglement based on three entanglement witnesses: one-tangle, two-tangle, and quantum Fisher information (QFI). We perform high-resolution INS measurements on Cs$_2$CoCl$_4$, a close realization of the $S=1/2$ transverse-field XXZ spin chain, where we can control entanglement using the magnetic field, and compare with density-matrix renormalization group calculations for validation. The three witnesses allow us to infer entanglement properties and make deductions about the quantum state in the material. We find QFI to be a particularly robust experimental probe of entanglement, whereas the one- and two-tangles require more careful analysis. Our results lay the foundation for a general entanglement detection protocol for quantum spin systems.

cond-mat.str-el↗

arXiv2020

Machine Learning for Magnetic Phase Diagrams and Inverse Scattering Problems

Machine learning promises to deliver powerful new approaches to neutron scattering from magnetic materials. Large scale simulations provide the means to realise this with approaches including spin-wave, Landau Lifshitz, and Monte Carlo methods. These approaches are shown to be effective at simulating magnetic structures and dynamics in a wide range of materials. Using large numbers of simulations the effectiveness of machine learning approaches are assessed. Principal component analysis and nonlinear autoencoders are considered with the latter found to provide a high degree of compression and to be highly suited to neutron scattering problems. Agglomerative heirarchical clustering in the latent space is shown to be effective at extracting phase diagrams of behavior and features in an automated way that aid understanding and interpretation. The autoencoders are also well suited to optimizing model parameters and were found to be highly advantageous over conventional fitting approaches including being tolerant of artifacts in untreated data. The potential of machine learning to automate complex data analysis tasks including the inversion of neutron scattering data into models and the processing of large volumes of multidimensional data is assessed. Directions for future developments are considered and machine learning argued to have high potential for impact on neutron science generally.

physics.comp-ph↗

arXiv2020

Machine Learning Assisted Insight to Spin Ice Dy$_2$Ti$_2$O$_7$

Complex behavior poses challenges in extracting models from experiment. An example is spin liquid formation in frustrated magnets like Dy$_2$Ti$_2$O$_7$. Understanding has been hindered by issues including disorder, glass formation, and interpretation of scattering data. Here, we use a novel automated capability to extract model Hamiltonians from data, and to identify different magnetic regimes. This involves training an autoencoder to learn a compressed representation of three-dimensional diffuse scattering, over a wide range of spin Hamiltonians. The autoencoder finds optimal matches according to scattering and heat capacity data and provides confidence intervals. Validation tests indicate that our optimal Hamiltonian accurately predicts temperature and field dependence of both magnetic structure and magnetization, as well as glass formation and irreversibility in Dy$_2$Ti$_2$O$_7$. The autoencoder can also categorize different magnetic behaviors and eliminate background noise and artifacts in raw data. Our methodology is readily applicable to other materials and types of scattering problems.

cond-mat.str-el↗

arXiv2020

Tuning quantum transport by controlling spin reorientations in Dirac semimetal candidates Eu$_{1-x}$Sr$_{x}$MnSb$_{2}$

Magnetic topological semimetals have attracted intense attention recently since these materials carry a great promise for potential applications in novel spintronic devices. Here, we report an intimate interplay between lattice, Eu magnetic order and topological semimetallic behavior in Eu$_{1-x}$Sr$_{x}$MnSb$_{2}$ driven by nonmagnetic Sr doping on magnetic Eu site. Different types of Eu spin reorientations are controllable by the Sr concentration, temperature or magnetic field, and coupled to the quantum transport properties of Dirac fermions generated by the 2D Sb layers. Our study opens a new pathway to achieving exotic magnetic order and topological semimetallic state via controlling spin reorientation. The effective strategy of substituting rare-earth site by nonmagnetic element demonstrated here may be applicable to the AMnCh$_{2}$ (A=rare-earth elements; Ch=Bi/Sb) family and a wide variation of other layered compounds involving spatially separated rare-earth and transition metal layers.

cond-mat.str-el↗

arXiv2020

Pulsed Neutron Spectroscopy of Low Dimensional Magnets: Past, Present, and Future

The early 1990s saw the first useful application of pulsed neutron spectroscopy to the study of excitations in low dimensional magnetic systems, with Roger Cowley as a key participant in important early experiments. Since that time the technique has blossomed as a powerful tool utilizing vastly improved neutron instrumentation coupled with more powerful pulsed sources. Here we review representative experiments illustrating some of the fascinating physics that has been revealed in quasi-one and two dimensional systems

cond-mat.str-el↗

arXiv2019

Finite field regime for a quantum spin liquid in $α$-RuCl$_3$

An external magnetic field can induce a transition in $α$-RuCl$_3$ from an ordered zigzag state to a disordered state that is possibly related to the Kitaev quantum spin liquid. Here we present new field dependent inelastic neutron scattering and magnetocaloric effect measurements implying the existence of an additional transition out of the quantum spin liquid phase at an upper field limit $B_u$. The neutron scattering shows three distinct regimes of magnetic response. In the low field ordered state the response shows magnon peaks; the intermediate field regime shows only continuum scattering, and above $B_u$ the response shows sharp magnon peaks at the lower bound of a strong continuum. Measurable dispersion of magnon modes along the $(0,0,L)$ direction implies non-negligible inter-plane interactions. Combining the magnetocaloric effect measurements with other data a $T-B$ phase diagram is constructed. The results constrain the range where one might expect to observe quantum spin liquid behavior in $α$-RuCl$_3$.

cond-mat.str-el↗

arXiv2016

Anomalous thermal decoherence in a quantum magnet measured with neutron spin-echo spectroscopy

The effect of temperature dependent asymmetric line broadening is investigated in Cu(NO$_3$)$_2\cdot$2.5D$_2$O, a model material for a 1-D bond alternating Heisenberg chain, using the high resolution neutron-resonance spin-echo (NRSE) technique. Inelastic neutron scattering experiments on dispersive excitations including phase sensitive measurements demonstrate the potential of NRSE to resolve line shapes, which are non-Lorentzian, opening up a new and hitherto unexplored class of experiments for the NRSE method beyond standard line width measurements. The particular advantage of NRSE is its direct access to the correlations in the time domain without convolution with the resolution function of the background spectrometer. This novel application of NRSE is very promising and establishes a basis for further experiments on different systems, since the results for Cu(NO$_3$)$_2\cdot$2.5D$_2$O are applicable to a broad range of quantum systems.

cond-mat.str-el↗