Quantum Hall effect based on Weyl orbits in Cd3As2.

Discovered decades ago, the quantum Hall effect remains one of the most studied phenomena in condensed matter physics and is relevant for research areas such as topological phases, strong electron correlations and quantum computing1-5. The quantized electron transport that is characteristic of the quantum Hall effect typically originates from chiral edge states-ballistic conducting channels that emerge when two-dimensional electron systems are subjected to large magnetic fields2. However, whether the quantum Hall effect can be extended to higher dimensions without simply stacking two-dimensional systems is unknown. Here we report evidence of a new type of quantum Hall effect, based on Weyl orbits in nanostructures of the three-dimensional topological semimetal Cd3As2. The Weyl orbits consist of Fermi arcs (open arc-like surface states) on opposite surfaces of the sample connected by one-dimensional chiral Landau levels along the magnetic field through the bulk6,7. This transport through the bulk results in an additional contribution (compared to stacked two-dimensional systems and which depends on the sample thickness) to the quantum phase of the Weyl orbit. Consequently, chiral states can emerge even in the bulk. To measure these quantum phase shifts and search for the associated chiral modes in the bulk, we conduct transport experiments using wedge-shaped Cd3As2 nanostructures with variable thickness. We find that the quantum Hall transport is strongly modulated by the sample thickness. The dependence of the Landau levels on the magnitude and direction of the magnetic field and on the sample thickness agrees with theoretical predictions based on the modified Lifshitz-Onsager relation for the Weyl orbits. Nanostructures of topological semimetals thus provide a way of exploring quantum Hall physics in three-dimensional materials with enhanced tunability.

Half-Metallic Transport and Spin-Polarized Tunneling through the van der Waals Ferromagnet Fe4GeTe2.

We examine the coherent spin-dependent transport properties of the van der Waals (vdW) ferromagnet Fe4GeTe2 using density functional theory combined with the nonequilibrium Green's function method. Our findings reveal that the conductance perpendicular to the layers is half-metallic, meaning that it is almost entirely spin-polarized. This property persists from the bulk to a single layer, even under significant bias voltages and with spin-orbit coupling. Additionally, using dynamical mean field theory for quantum transport, we demonstrate that electron correlations are important for magnetic properties but minimally impact the conductance, preserving almost perfect spin-polarization. Motivated by these results, we then study the tunnel magnetoresistance (TMR) in a magnetic tunnel junction consisting of two Fe4GeTe2 layers with the vdW gap acting as an insulating barrier. We predict a TMR ratio of ∼500%, which can be further enhanced by increasing the number of Fe4GeTe2 layers in the junction.

Machine-Learning-Assisted Construction of Ternary Convex Hull Diagrams.

In the search for novel intermetallic ternary alloys, much of the effort goes into performing a large number of ab initio calculations covering a wide range of compositions and structures. These are essential to building a reliable convex hull diagram. While density functional theory (DFT) provides accurate predictions for many systems, its computational overheads set a throughput limit on the number of hypothetical phases that can be probed. Here, we demonstrate how an ensemble of machine-learning (ML) spectral neighbor-analysis potentials (SNAPs) can be integrated into a workflow for the construction of accurate ternary convex hull diagrams, highlighting regions that are fertile for materials discovery. Our workflow relies on using available binary-alloy data both to train the SNAP models and to create prototypes for ternary phases. From the prototype structures, all unique ternary decorations are created and used to form a pool of candidate compounds. The SNAPs ensemble is then used to prerelax the structures and screen the most favorable prototypes before using DFT to build the final phase diagram. As constructed, the proposed workflow relies on no extra first-principles data to train the ML surrogate model and yields a DFT-level accurate convex hull. We demonstrate its efficacy by investigating the Cu-Ag-Au and Mo-Ta-W ternary systems.

PMUT-Based System for Continuous Monitoring of Bolted Joints Preload.

In this paper, we present a bolt preload monitoring system, including the system architecture and algorithms. We show how Finite Element Method (FEM) simulations aided the design and how we processed signals to achieve experimental validation. The preload is measured using a Piezoelectric Micromachined Ultrasonic Transducer (PMUT) in pulse-echo mode, by detecting the Change in Time-of-Flight (CTOF) of the acoustic wave generated by the PMUT, between no-load and load conditions. We performed FEM simulations to analyze the wave propagation inside the bolt and understand the effect of different configurations and parameters, such as transducer bandwidth, transducer position (head/tip), presence or absence of threads, as well as the frequency of the acoustic waves. In order to couple the PMUT to the bolt, a novel assembly process involving the deposition of an elastomeric acoustic impedance matching layer was developed. We achieved, for the first time with PMUTs, an experimental measure of bolt preload from the CTOF, with a good signal-to-noise ratio. Due to its low cost and small size, this system has great potential for use in the field for continuous monitoring throughout the operative life of the bolt.

Enabling Room-Temperature Triferroic Coupling in Dual Transition-Metal Dichalcogenide Monolayers Via Electronic Asymmetry.

Triferroic compounds are the ideal platform for multistate information devices but are rare in the two-dimensional (2D) form, and none of them can maintain macroscopic order at room temperature. Herein, we propose a general strategy for achieving 2D triferroicity by imposing electric polarization into a ferroelastic magnet. Accordingly, dual transition-metal dichalcogenides, for example, 1T'-CrCoS4, are demonstrated to display room-temperature triferroicity. The magnetic order of 1T'-CrCoS4 undergoes a magnetic transition during the ferroic switching, indicating robust triferroic magnetoelectric coupling. In addition, the negative out-of-plane piezoelectricity and strain-tunable magnetic anisotropy make the 1T'-CrCoS4 monolayer a strong candidate for practical applications. Following the proposed scheme, a new class of 2D room-temperature triferroic materials is introduced, providing a promising platform for advanced spintronics.

ZnSe and ZnTe as tunnel barriers for Fe-based spin valves.

Owing to their use in the optoelectronic industry, we investigate whether ZnSe and ZnTe can be utilised as tunnel barrier materials in magnetic spin valves. We perform ab initio electronic structure and linear response transport calculations based on self-interaction-corrected density functional theory for both Fe/ZnSe/Fe and Fe/ZnTe/Fe junctions. In the Fe/ZnSe/Fe junction the transport is tunneling-like and a symmetry-filtering mechanism is at play, implying that only the majority spin electrons with Δ1 symmetry are transmitted with large probability, resulting in a potentially large tunneling magnetoresistance (TMR) ratio. As such, the transport characteristics are similar to those of the Fe/MgO/Fe junction, although the TMR ratio is lower for tunnel barriers of similar thickness due to the smaller bandgap of ZnSe as compared to that of MgO. In the Fe/ZnTe/Fe junction the Fermi level is pinned at the bottom of the conduction band of ZnTe and only a giant magnetoresistance effect is found. Our results provide evidence that chalcogenide-based tunnel barriers can be utilised in spintronics devices.

Robust covalent pyrazine anchors forming highly conductive and polarity-tunable molecular junctions with carbon electrodes.

In molecular electronics, electrode-molecule anchoring strategies play a crucial role in the design of stable and high-performance functional single-molecule devices. Herein, we employ aromatic pyrazine as anchors to connect a central anthracene molecule to carbon electrodes including graphene and armchair single-walled carbon nanotubes (SWCNTs), and theoretically investigate their atomic structures and electronic transport properties. These molecular junctions can be constructed via condensation reactions of the central molecules terminated with ortho-phenylenediamines with ortho-quinone-functionalized nanogaps of graphene and SWCNT electrodes. With two direct C-N covalent bonds connecting the central molecule site-selectively to carbon electrodes in a coplanar way, pyrazine anchors are advantageous for forming stable and structurally well-defined molecular junctions, being expected to reduce the uncertainty about the electrode-molecule linkage motifs. The junction transport is highly efficient due to the coplanar geometry and the ensuing strong π-type molecule-electrode electronic coupling. Furthermore, our calculations show that molecular junctions with pyrazine anchors and carbon electrodes are usually n-type electronic devices; upon hydrogenation of pyridinic nitrogen atoms, the device polarity can be tuned to p-type, indicating that the pyrazine anchors can also serve as a powerful platform for tailoring in situ the polarity of charge carriers in carbon-electrode molecular electronic devices.

Using Weakly Supervised Deep Learning to Classify and Segment Single-Molecule Break-Junction Conductance Traces.

In order to design molecular electronic devices with high performance and stability, it is crucial to understand their structure-to-property relationships. Single-molecule break junction measurements yield a large number of conductance-distance traces, which are inherently highly stochastic. Here we propose a weakly supervised deep learning algorithm to classify and segment these conductance traces, a method that is mainly based on transfer learning with the pretrain-finetune technique. By exploiting the powerful feature extraction capabilities of the pretrained VGG-16 network, our convolutional neural network model not only achieves high accuracy in the classification of the conductance traces, but also segments precisely the conductance plateau from an entire trace with very few manually labeled traces. Thus, we can produce a more reliable estimation of the junction conductance and quantify the junction stability. These findings show that our model has achieved a better accuracy-to-manpower efficiency balance, opening up the possibility of using weakly supervised deep learning approaches in the studies of single-molecule junctions.

Ultrahigh conductivity in Weyl semimetal NbAs nanobelts.

In two-dimensional (2D) systems, high mobility is typically achieved in low-carrier-density semiconductors and semimetals. Here, we discover that the nanobelts of Weyl semimetal NbAs maintain a high mobility even in the presence of a high sheet carrier density. We develop a growth scheme to synthesize single crystalline NbAs nanobelts with tunable Fermi levels. Owing to a large surface-to-bulk ratio, we argue that a 2D surface state gives rise to the high sheet carrier density, even though the bulk Fermi level is located near the Weyl nodes. A surface sheet conductance up to 5-100 S per □ is realized, exceeding that of conventional 2D electron gases, quasi-2D metal films, and topological insulator surface states. Corroborated by theory, we attribute the origin of the ultrahigh conductance to the disorder-tolerant Fermi arcs. The evidenced low-dissipation property of Fermi arcs has implications for both fundamental study and potential electronic applications.

Multiple spin-phonon relaxation pathways in a Kramer single-ion magnet.

We present a first-principles investigation of spin-phonon relaxation in a molecular crystal of Co2+ single-ion magnets. Our study combines electronic structure calculations with machine-learning force fields and unravels the nature of both the Orbach and the Raman relaxation channels in terms of atomistic processes. We find that although both mechanisms are mediated by the excited spin states, the low temperature spin dynamics is dominated by phonons in the THz energy range, which partially suppress the benefit of having a large magnetic anisotropy. This study also determines the importance of intra-molecular motions for both the relaxation mechanisms and paves the way to the rational design of a new generation of single-ion magnets with tailored spin-phonon coupling.