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.

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.

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.

First-Principles Prediction of a Room-Temperature Ferromagnetic Janus VSSe Monolayer with Piezoelectricity, Ferroelasticity, and Large Valley Polarization.

Inspired by recent experiments on the successful fabrication of monolayer Janus transition-metal dichalcogenides [ Lu , A.-Y. ; Nat. Nanotechnol. 2017 , 12 , ( 8 ), 744 and ferromagnetic VSe2 [ Bonilla , M. ; Nat. Nanotechnol. 2018 , 13 , ( 4 ), 289 ], we predict a highly stable room-temperature ferromagnetic Janus monolayer (VSSe) by density functional theory methods and further confirmed the stability by a global minimum search with the particle-swarm optimization method. The VSSe monolayer exhibits a large valley polarization due to the broken space- and time-reversal symmetry. Moreover, its low symmetry C3 v point group results in giant in-plane piezoelectric polarization. Most interestingly, a strain-driven 90° lattice rotation is found in the magnetic VSSe monolayer with an extremely high reversal strain (73%), indicating an intrinsic ferroelasticity. The combination of piezoelectricity and valley polarization make magnetic 2D Janus VSSe a tantalizing material for potential applications in nanoelectronics, optoelectronics, and valleytronics.

First-Principles Investigation of Spin-Phonon Coupling in Vanadium-Based Molecular Spin Quantum Bits.

Paramagnetic molecules can show long spin-coherence times, which make them good candidates as quantum bits (qubits). Reducing the efficiency of the spin-phonon interaction is the primary challenge toward achieving long coherence times over a wide temperature range in soft molecular lattices. The lack of a microscopic understanding about the role of vibrations in spin relaxation strongly undermines the possibility of chemically designing better-performing molecular qubits. Here we report a first-principles characterization of the main mechanism contributing to the spin-phonon coupling for a class of vanadium(IV) molecular qubits. Post-Hartree-Fock and density functional theory methods are used to determine the effect of both intermolecular and intramolecular vibrations on modulation of the Zeeman energy for four molecules showing different coordination geometries and ligands. This comparative study provides the first insight into the role played by coordination geometry and ligand-field strength in determining the spin-lattice relaxation time of molecular qubits, opening an avenue to the rational design of new compounds.

Correction: Exploring new approaches towards the formability of mixed-ion perovskites by DFT and machine learning.

Correction for 'Exploring new approaches towards the formability of mixed-ion perovskites by DFT and machine learning' by Heesoo Park et al., Phys. Chem. Chem. Phys., 2019, DOI: 10.1039/c8cp06528d.

Exploring new approaches towards the formability of mixed-ion perovskites by DFT and machine learning.

Recent years have witnessed a growing effort in engineering and tuning the properties of hybrid halide perovskites as light absorbers. These have led to the successful enhancement of their stability, a feature that is often counterbalanced by a reduction of their power-conversion efficiency. In order to provide a systematic analysis of the structure-property relationships of this class of compounds we have performed density functional theory calculations exploring fully inorganic ABC3 chalcogenide (I-V-VI3), halide (I-II-VII3) and hybrid perovskites. Special attention has been given to structures featuring three-dimensional BC6 octahedral networks because of their efficient carrier transport properties. In particular we have carefully analyzed the role of BC6 octahedral deformations, rotations and tilts in the thermodynamic stability and optical properties of the compounds. By using machine learning algorithms we have estimated the relations between the octahedral deformation and the bandgap, and established a similarity map among all the calculated compounds.

Learn-and-Match Molecular Cations for Perovskites.

Forecasting the structural stability of hybrid organic/inorganic compounds, where polyatomic molecules replace atoms, is a challenging task; the composition space is vast, and the reference structure for the organic molecules is ambiguously defined. In this work, we use a range of machine-learning algorithms, constructed from state-of-the-art density functional theory data, to conduct a systematic analysis on the likelihood of a given cation to be housed in the perovskite structure. In particular, we consider both ABC3 chalcogenide (I-V-VI3) and halide (I-II-VII3) perovskites. We find that the effective atomic radius and the number of lone pairs residing on the A-site cation are sufficient features to describe the perovskite phase stability. Thus, the presented machine-learning approach provides an efficient way to map the phase stability of the vast class of compounds, including situations where a cation mixture replaces a single A-site cation. This work demonstrates that advanced electronic structure theory combined with machine-learning analysis can provide an efficient strategy superior to the conventional trial-and-error approach in materials design.

Silver Tarnishing Mechanism Revealed by Molecular Dynamics Simulations.

The mechanism of silver-oxygen and silver-sulfur reactions is revealed by means of molecular dynamics simulations, performed with reactive force fields purposely built and extensively tested against quantum-chemical results. Different reaction mechanisms and rates for Ag-O and Ag-S emerge. This study solves the long-lasting question why silver exposed to the environment is strongly vulnerable to sulfur corrosion (tarnishing) but hardly reacts with O2 , despite the thermodynamic prediction that both oxide and sulfide should form. The reliability of the simulation results is confirmed by the agreement with a multitude of experimental results from the literature.

Elucidating the Impact of Chalcogen Content on the Photovoltaic Properties of Oxychalcogenide Perovkskites: NaMO3-x Qx (M=Nb, Ta; Q=S, Se, Te).

In the quest for nontoxic and stable perovskites for solar cells, we have conducted a systematic study of the effect of chalcogen content in oxychalcogenide perovskite by using DFT and quasi-particle perturbation theory. We explored the changes in the electronic structure due to the substitution of O atoms in NaNbO3 and NaTaO3 perovskite structures with various chalcogens (S, Se, Te) at different concentrations. Interestingly, the introduction of the chalcogen atoms resulted in a drastic reduction in the electronic band gap, which made some of the compounds fall within the visible range of the solar spectrum. In addition, our analysis of the electronic structure shows that the optical transition becomes direct as a result of the strong hybridization between the orbitals of the transition metal and those of the chalcogen ion, in contrast to the indirect band feature of NaNbO3 and NaTaO3 . We identified candidates with a high theoretical solar conversion efficiency that approached the Shockley-Queisser limit, which makes them suitable for thin-film solar cell applications. The present work serves as a guideline for experimental efforts by identifying the chalcogen content that should be targeted during the synthetic route of thermodynamically stable and strongly photoactive absorbers for oxychalcogenide perovskites in thin-film solar cells.

AFLOW-CHULL: Cloud-Oriented Platform for Autonomous Phase Stability Analysis.

A priori prediction of phase stability of materials is a challenging practice, requiring knowledge of all energetically competing structures at formation conditions. Large materials repositories-housing properties of both experimental and hypothetical compounds-offer a path to prediction through the construction of informatics-based, ab initio phase diagrams. However, limited access to relevant data and software infrastructure has rendered thermodynamic characterizations largely peripheral, despite their continued success in dictating synthesizability. Herein, a new module is presented for autonomous thermodynamic stability analysis, implemented within the open-source, ab initio framework AFLOW. Powered by the AFLUX Search-API, AFLOW-CHULL leverages data of more than 1.8 million compounds characterized in the AFLOW.org repository, and can be employed locally from any UNIX-like computer. The module integrates a range of functionality: the identification of stable phases and equivalent structures, phase coexistence, measures for robust stability, and determination of decomposition reactions. As a proof of concept, thermodynamic characterizations have been performed for more than 1300 binary and ternary systems, enabling the identification of several candidate phases for synthesis based on their relative stability criterion-including 17 promising C15 b-type structures and 2 half-Heuslers. In addition to a full report included herein, an interactive, online web application has been developed showcasing the results of the analysis and is located at aflow.org/aflow-chull .

Ab initio surface properties of Ag-Sn alloys: implications for lead-free soldering.

Ag and Sn are the major components of solder alloys adopted to assemble printed circuit boards. The qualities that make them the alloys of choice for the modern electronic industry are related to their physical and chemical properties. For corrosion resistance and solderability, surface properties are particularly important. Yet, atomic-level information about the surfaces of these alloys is not known. Here we fill this gap by presenting an extensive ab initio investigation of composition, energetics, structure and reactivity of Ag-Sn alloy surfaces. The structure and stability of various surfaces is evaluated, and the main factors determining the energetics of surface formation are uncovered. Oxygen and sulphur chemisorptions are studied and discussed in the framework of corrosion tendency, an important issue for printed circuit boards. Adsorption energy trends are rationalized based on the analysis of structural and electronic features.

Effect of molecular conformations on the electronic transport in oxygen-substituted alkanethiol molecular junctions.

The relationship between the molecular structure and the electronic transport properties of molecular junctions based on thiol-terminated oligoethers, which are obtained by replacing every third methylene unit in the corresponding alkanethiols with an oxygen atom, is investigated by employing the non-equilibrium Green's function formalism combined with density functional theory. Our calculations show that the low-bias conductance depends strongly on the conformation of the oligoethers in the junction. Specifically, in the cases of trans-extended conformation, the oxygen-dominated transmission peaks are very sharp and well below the Fermi energy, EF, thus hardly affect the transmission around EF; the Au-S interface hybrid states couple with σ-bonds in the molecular backbone forming the conduction channel at EF, resulting in a conductance decay against the molecular length close to that for alkanethiols. By contrast, for junctions with oligoethers in helical conformations, some π-type oxygen orbitals coupling with the Au-S interface hybrid states contribute to the transmission around EF. The molecule-electrode electronic coupling is also enhanced at the non-thiol side due to the specific spatial orientation introduced by the twist of the molecular backbone. This leads to a much smaller conductance decay constant. Our findings highlight the important role of the molecular conformation of oligoethers in their electronic transport properties and are also helpful for the design of molecular wires with heteroatom-substituted alkanethiols.

Influence of the dipolar interactions on the relative stability in spin crossover systems.

Molecules exhibiting a spin-crossover transition have been proposed for a number of applications such as molecular switches, spintronic tunable interfaces, and single molecule gates. Both the rational design of new spin-crossover systems and the improvement of the properties of the already existing ones require a theoretical understanding of the relative energy of the high (HS) and low spin state (LS) molecules in the solid-state. This has proved to be very challenging so far. Here, we shed some light on the importance of considering the symmetry and the geometry of the crystallographic cell to correctly evaluate the influence of the dipolar interactions on the relative energies of the molecular complex in both different spin states. Moreover, in the case of Fe(SCN)2 (phen)2 dipolar interactions are found to play an important role for the stabilization of the LS complex. © 2016 Wiley Periodicals, Inc.

First-Principles Prediction of Spin-Polarized Multiple Dirac Rings in Manganese Fluoride.

Spin-polarized materials with Dirac features have sparked great scientific interest due to their potential applications in spintronics. But such a type of structure is very rare and none has been fabricated. Here, we investigate the already experimentally synthesized manganese fluoride (MnF_{3}) as a novel spin-polarized Dirac material by using first-principles calculations. MnF_{3} exhibits multiple Dirac cones in one spin orientation, while it behaves like a large gap semiconductor in the other spin channel. The estimated Fermi velocity for each cone is of the same order of magnitude as that in graphene. The 3D band structure further reveals that MnF_{3} possesses rings of Dirac nodes in the Brillouin zone. Such a spin-polarized multiple Dirac ring feature is reported for the first time in an experimentally realized material. Moreover, similar band dispersions can be also found in other transition metal fluorides (e.g., CoF_{3}, CrF_{3}, and FeF_{3}). Our results highlight a new interesting single-spin Dirac material with promising applications in spintronics and information technologies.