2,088 research outputs found
Electronic Transport on the Shastry-Sutherland Lattice in Ising-type Rare Earth Tetraborides
In the presence of a magnetic field frustrated spin systems may exhibit
plateaus at fractional values of saturation magnetization. Such plateau states
are stabilized by classical and quantum mechanisms including order-by-disorder,
triplon crystallization, and various competing order effects. In the case of
electrically conducting systems, free electrons represent an incisive probe for
the plateau states. Here we study the electrical transport of Ising-type rare
earth tetraborides B (Er, Tm), a metallic Shastry-Sutherland lattice
showing magnetization plateaus. We find that the longitudinal and transverse
resistivities reflect scattering with both the static and dynamic plateau
structure. We model these results consistently with the expected strong
uniaxial anisotropy in a quantitative level, providing a framework for the
study of plateau states in metallic frustrated systems.Comment: 18 pages, 5 figure
Transport Signatures of Fermi Surface Topology Change in BiTeI
We report a quantum magnetotransport signature of a change in Fermi surface
topology in the Rashba semiconductor BiTeI with systematic tuning of the Fermi
level . Beyond the quantum limit, we observe a marked increase/decrease in
electrical resistivity when is above/below the Dirac node that we show
originates from the Fermi surface topology. This effect represents a
measurement of the electron distribution on the low-index () Landau
levels and is uniquely enabled by the finite bulk dispersion along the
-axis and strong Rashba spin-orbit coupling strength of the system. The
Dirac node is independently identified by Shubnikov-de Haas oscillations as a
vanishing Fermi surface cross section at . Additionally we find that the
violation of Kohler's rule allows a distinct insight into the temperature
evolution of the observed quantum magnetoresistance effects.Comment: 12 pages, 4 figure
Extreme Magnetoresistance in Magnetic Rare Earth Monopnictides
The acute sensitivity of the electrical resistance of certain systems to
magnetic fields known as extreme magnetoresistance (XMR) has recently been
explored in a new materials context with topological semimetals. Exemplified by
WTe and rare earth monopnictide La(Sb,Bi), these systems tend to be
non-magnetic, nearly compensated semimetals and represent a platform for large
magnetoresistance driven by intrinsic electronic structure. Here we explore
electronic transport in magnetic members of the latter family of semimetals and
find that XMR is strongly modulated by magnetic order. In particular, CeSb
exhibits XMR in excess of % at fields of 9 T while the
magnetoresistance itself is non-monotonic across the various magnetic phases
and shows a transition from negative magnetoresistance to XMR with field above
magnetic ordering temperature . The magnitude of the XMR is larger than
in other rare earth monopnictides including the non-magnetic members and
follows an non-saturating power law to fields above 30 T. We show that the
overall response can be understood as the modulation of conductivity by the Ce
orbital state and for intermediate temperatures can be characterized by an
effective medium model. Comparison to the orbitally quenched compound GdBi
supports the correlation of XMR with the onset of magnetic ordering and
compensation and highlights the unique combination of orbital inversion and
type-I magnetic ordering in CeSb in determining its large response. These
findings suggest a paradigm for magneto-orbital control of XMR and are relevant
to the understanding of rare earth-based correlated topological materials.Comment: 21 pages, 6 figure
Essential gene pathways for glioblastoma stem cells: clinical implications for prevention of tumor recurrence.
Glioblastoma (World Health Organization/WHO grade IV) is the most common and most aggressive adult glial tumor. Patients with glioblastoma, despite being treated with gross total resection and post-operative radiation/chemotherapy, will almost always develop tumor recurrence. Glioblastoma stem cells (GSC), a minor subpopulation within the tumor mass, have been recently characterized as tumor-initiating cells and hypothesized to be responsible for post-treatment recurrence because of their enhanced radio-/chemo-resistant phenotype and ability to reconstitute tumors in mouse brains. Genome-wide expression profile analysis uncovered molecular properties of GSC distinct from their differentiated, proliferative progeny that comprise the majority of the tumor mass. In contrast to the hyperproliferative and hyperangiogenic phenotype of glioblastoma tumors, GSC possess neuroectodermal properties and express genes associated with neural stem cells, radial glial cells, and neural crest cells, as well as portray a migratory, quiescent, and undifferentiated phenotype. Thus, cell cycle-targeted radio-chemotherapy, which aims to kill fast-growing tumor cells, may not completely eliminate glioblastoma tumors. To prevent tumor recurrence, a strategy targeting essential gene pathways of GSC must be identified and incorporated into the standard treatment regimen. Identifying intrinsic and extrinsic cues by which GSC maintain stemness properties and sustain both tumorigenesis and anti-apoptotic features may provide new insights into potentially curative strategies for treating brain cancers
Deep Learning Computed Tomography based on the Defrise and Clack Algorithm
This study presents a novel approach for reconstructing cone beam computed
tomography (CBCT) for specific orbits using known operator learning. Unlike
traditional methods, this technique employs a filtered backprojection type
(FBP-type) algorithm, which integrates a unique, adaptive filtering process.
This process involves a series of operations, including weightings,
differentiations, the 2D Radon transform, and backprojection. The filter is
designed for a specific orbit geometry and is obtained using a data-driven
approach based on deep learning. The approach efficiently learns and optimizes
the orbit-related component of the filter. The method has demonstrated its
ability through experimentation by successfully learning parameters from
circular orbit projection data. Subsequently, the optimized parameters are used
to reconstruct images, resulting in outcomes that closely resemble the
analytical solution. This demonstrates the potential of the method to learn
appropriate parameters from any specific orbit projection data and achieve
reconstruction. The algorithm has demonstrated improvement, particularly in
enhancing reconstruction speed and reducing memory usage for handling specific
orbit reconstruction
Creating Weyl nodes and controlling their energy by magnetization rotation
As they do not rely on the presence of any crystal symmetry, Weyl nodes are
robust topological features of an electronic structure that can occur at any
momentum and energy. Acting as sinks and sources of Berry curvature, Weyl nodes
have been predicted to strongly affect the transverse electronic response, like
in the anomalous Hall or Nernst effects. However, to observe large anomalous
effects the Weyl nodes need to be close to or at the Fermi-level, which implies
the band structure must be tuned by an external parameter, e.g. chemical doping
or pressure. Here we show that in a ferromagnetic metal tuning of the Weyl node
energy and momentum can be achieved by rotation of the magnetization. Taking
CoSnS as an example, we use electronic structure calculations based
on density-functional theory to show that not only new Weyl fermions can be
created by canting the magnetization away from the easy axis, but also that the
Weyl nodes can be driven exactly to the Fermi surface. We also show that the
dynamics in energy and momentum of the Weyl nodes strongly affect the
calculated anomalous Hall and Nernst conductivities.Comment: Supp. Material adde
Measurement of the magnetic octupole susceptibility of PrV2Al20
In the electromagnetic multipole expansion, magnetic octupoles are the
subsequent order of magnetic multipoles allowed in centrosymmetric systems,
following the more commonly observed magnetic dipoles. As order parameters in
condensed matter systems, magnetic octupoles have been experimentally elusive.
In particular, the lack of simple external fields that directly couple to them
makes their experimental detection challenging. Here, we demonstrate a
methodology for probing the magnetic octupole susceptibility using a product of
magnetic field and shear strain to couple to the
octupolar fluctuations, while using an adiabatic elastocaloric effect to probe
the response to this composite effective field. We observe a Curie-Weiss
behavior in the obtained octupolar susceptibility of \ce{PrV2Al20} up to
temperatures approximately forty times the putative octupole ordering
temperature. Our results demonstrate the presence of magnetic octupole
fluctuations in the particular material system, and more broadly highlight how
anisotropic strain can be combined with magnetic fields to formulate a
versatile probe to observe otherwise elusive emergent `hidden' electronic
orders.Comment: 7 pages, 3 figure
The Role of Reference Points in Machine-Learned Atomistic Simulation Models
This paper introduces the Chemical Environment Modeling Theory (CEMT), a
novel, generalized framework designed to overcome the limitations inherent in
traditional atom-centered Machine Learning Force Field (MLFF) models, widely
used in atomistic simulations of chemical systems. CEMT demonstrated enhanced
flexibility and adaptability by allowing reference points to exist anywhere
within the modeled domain and thus, enabling the study of various model
architectures. Utilizing Gaussian Multipole (GMP) featurization functions,
several models with different reference point sets, including finite difference
grid-centered and bond-centered models, were tested to analyze the variance in
capabilities intrinsic to models built on distinct reference points. The
results underscore the potential of non-atom-centered reference points in force
training, revealing variations in prediction accuracy, inference speed and
learning efficiency. Finally, a unique connection between CEMT and real-space
orbital-free finite element Density Functional Theory (FE-DFT) is established,
and the implications include the enhancement of data efficiency and robustness.
It allows the leveraging of spatially-resolved energy densities and charge
densities from FE-DFT calculations, as well as serving as a pivotal step
towards integrating known quantum-mechanical laws into the architecture of ML
models
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