Quantitative analysis of printed nanostructured networks using high-resolution 3D FIB-SEM nanotomography.

Networks of solution-processed nanomaterials are becoming increasingly important across applications in electronics, sensing and energy storage/generation. Although the physical properties of these devices are often completely dominated by network morphology, the network structure itself remains difficult to interrogate. Here, we utilise focused ion beam - scanning electron microscopy nanotomography (FIB-SEM-NT) to quantitatively characterise the morphology of printed nanostructured networks and their devices using nanometre-resolution 3D images. The influence of nanosheet/nanowire size on network structure in printed films of graphene, WS2 and silver nanosheets (AgNSs), as well as networks of silver nanowires (AgNWs), is investigated. We present a comprehensive toolkit to extract morphological characteristics including network porosity, tortuosity, specific surface area, pore dimensions and nanosheet orientation, which we link to network resistivity. By extending this technique to interrogate the structure and interfaces within printed vertical heterostacks, we demonstrate the potential of this technique for device characterisation and optimisation.

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.

Stiffness and Atomic-Scale Friction in Superlubricant MoS2 Bilayers.

Molecular dynamics simulations, performed with chemically accurate ab initio machine-learning force fields, are used to demonstrate that layer stiffness has profound effects on the superlubricant state of two-dimensional van der Waals heterostructures. We engineer bilayers of different rigidity but identical interlayer sliding energy surface and show that a 2-fold increase in the intralayer stiffness reduces the friction by a factor of ∼6. Two sliding regimes as a function of the sliding velocity are found. At a low velocity, the heat generated by the motion is efficiently exchanged between the layers and the friction is independent of the layer order. In contrast, at a high velocity, the friction heat flux cannot be exchanged fast enough and a buildup of significant temperature gradients between the layers is observed. In this situation, the temperature profile depends on whether the slider is softer than the substrate.

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.

Highly degenerate 2D ferroelectricity in pore decorated covalent/metal organic frameworks.

Two-dimensional (2D) ferroelectricity, a fundamental concept in low-dimensional physics, serves as the basis of non-volatile information storage and various electronic devices. Conventional 2D ferroelectric (FE) materials are usually two-fold degenerate, meaning that they can only store two logical states. In order to break such limitation, a new concept of highly degenerate ferroelectricity with multiple FE states (more than 2) coexisting in a single 2D material is proposed. This is obtained through the asymmetrical decoration of porous covalent/metal organic frameworks (COFs/MOFs). Using first-principles calculations and Monte Carlo (MC) simulations, Li-decorated 2D Cr(pyz)2 is systematically explored as a prototype of highly degenerate 2D FE materials. We show that 2D FE Li0.5Cr(pyz)2 and LiCr(pyz)2 are four-fold and eight-fold degenerate, respectively, with sizable spontaneous electric polarization that can be switched across low transition barriers. In particular, the coupling between neighbouring electric dipoles in LiCr(pyz)2 induces novel ferroelectricity-controlled ferroelastic transition and direction-controllable hole transport channels. Moreover, three-fold and six-fold degenerate ferroelectricity is also demonstrated in P-decorated g-C3N4 and Ru-decorated C2N, respectively. Our work presents a general route to obtain highly degenerate 2D ferroelectricity, which goes beyond the two-state paradigm of traditional 2D FE materials and substantially broadens the applications of 2D FE compounds.

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.

A rule-free workflow for the automated generation of databases from scientific literature.

In recent times, transformer networks have achieved state-of-the-art performance in a wide range of natural language processing tasks. Here we present a workflow based on the fine-tuning of BERT models for different downstream tasks, which results in the automated extraction of structured information from unstructured natural language in scientific literature. Contrary to existing methods for the automated extraction of structured compound-property relations from similar sources, our workflow does not rely on the definition of intricate grammar rules. Hence, it can be adapted to a new task without requiring extensive implementation efforts and knowledge. We test our data-extraction workflow by automatically generating a database for Curie temperatures and one for band gaps. These are then compared with manually curated datasets and with those obtained with a state-of-the-art rule-based method. Furthermore, in order to showcase the practical utility of the automatically extracted data in a material-design workflow, we employ them to construct machine-learning models to predict Curie temperatures and band gaps. In general, we find that, although more noisy, automatically extracted datasets can grow fast in volume and that such volume partially compensates for the inaccuracy in downstream tasks.

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.

Hydrogen-Intercalated 2D Magnetic Bilayer: Controlled Magnetic Phase Transition and Half-Metallicity via Ferroelectric Switching.

Electrically controlled magnetism in two-dimensional (2D) multiferroics is highly desirable for both fundamental research and the future development of low-power nanodevices. Herein, inspired by the recently experimentally realized 2D antiferromagnetic MnPSe3 [ Nat. Nanotechnol. 2021, 16 (7), 782] and guided by a heteromagnetic structural design, we engineer strong magnetoelectric coupling in a hydrogen-intercalated 2D MnPSe3 bilayer. Hydrogen functionalization breaks the centrosymmetry of bilayer MnPSe3, leading to out-of-plane ferroelectricity. Moreover, there is a phase transition from antiferromagnetic semiconductor to ferromagnetic half-metal in the H-bonded MnPSe3 layer, while the other remains antiferromagnetic and semiconducting. When reversing the electrical polarization, the intercalated H atom can flip between the top and bottom layers with an ultralow switching barrier, which allows one to tune the magnetic order and conductivity of the individual layers via an external electric field. Our results pave a new avenue to realize strong magnetoelectric coupling in single-phase multiferroic material. The ferroelectricity-controlled magnetic phase transition and half-metallicity offer promising applications in nanoscale spintronics such as electrically written and magnetically read memories.

Computational design of magnetic molecules and their environment using quantum chemistry, machine learning and multiscale simulations.

Having served as a playground for fundamental studies on the physics of d and f electrons for almost a century, magnetic molecules are now becoming increasingly important for technological applications, such as magnetic resonance, data storage, spintronics and quantum information. All of these applications require the preservation and control of spins in time, an ability hampered by the interaction with the environment, namely with other spins, conduction electrons, molecular vibrations and electromagnetic fields. Thus, the design of a novel magnetic molecule with tailored properties is a formidable task, which does not only concern its electronic structures but also calls for a deep understanding of the interaction among all the degrees of freedom at play. This Review describes how state-of-the-art ab initio computational methods, combined with data-driven approaches to materials modelling, can be integrated into a fully multiscale strategy capable of defining design rules for magnetic molecules.

In Situ Tuning of the Charge-Carrier Polarity in Imidazole-Linked Single-Molecule Junctions.

Manipulating the nature of the charge carriers at the single-molecule level is one of the major challenges of molecular electronics. Using first-principles quantum transport calculations, we have investigated the electronic transport properties of imidazole-linked single-molecule junctions and identified the hydrogen atom bonded to the pyrrole-like nitrogen in imidazole as a switch to tune the polarity of the charge carriers. Our calculations show that the chemical nature of the imidazole anchors is dramatically altered by dehydrogenation, which changes the dominant charge carriers from electrons to holes. It is also revealed that upon dehydrogenation the interfacial Au-N bonds are modified from donor-acceptor-like to covalent, along with a significant promotion of the low-bias conductance and the junction stability. At variance with other traditional methods that always require drastic modifications of the junction structure, our findings provide a promising approach to tailor in situ the polarity of charge carriers in 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.

Prediction of room-temperature ferromagnetism and large perpendicular magnetic anisotropy in a planar hypercoordinate FeB3 monolayer.

Two-dimensional (2D) magnets simultaneously possessing a high transition temperature and large perpendicular magnetic anisotropy are extremely rare, but essential for highly efficient spintronic applications. By using ab initio and global minimization approaches, we for the first time report a completely planar hypercoordinate metalloborophene (α-FeB3) with high stability, unusual stoichiometry and exceptional magnetoelectronic properties. The α-FeB3 monolayer exhibits room-temperature ferromagnetism (Tc = 480 K), whose origin is first revealed by the B-mediated RKKY interaction in the 2D regime. Its perpendicular magnetic anisotropy is almost six times larger than that of the experimentally realized 2D CrI3 and Fe3GeTe2. Moreover, metallic α-FeB3 shows n- and p-type Dirac transport with a high Fermi velocity in both spin channels. Our results not only highlight a promising 2D ferromagnet for advanced spintronics, but also pave the way for exploring novel 2D magnetism in boron-based magnetic allotropes.

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.

The Limit of Spin Lifetime in Solid-State Electronic Spins.

The development of spin qubits for quantum technologies requires their protection from the main source of finite-temperature decoherence: atomic vibrations. Here we eliminate one of the main barriers to the progress in this field by providing a complete first-principles picture of spin relaxation that includes up to two-phonon processes. Our method is based on machine learning and electronic structure theory and makes the prediction of spin lifetime in realistic systems feasible. We study a prototypical vanadium-based molecular qubit and reveal that the spin lifetime at high temperature is limited by Raman processes due to a small number of THz intramolecular vibrations. These findings effectively change the conventional understanding of spin relaxation in this class of materials and open new avenues for the rational design of long-living spin systems.

Neutral excitation density-functional theory: an efficient and variational first-principles method for simulating neutral excitations in molecules.

We introduce neutral excitation density-functional theory (XDFT), a computationally light, generally applicable, first-principles technique for calculating neutral electronic excitations. The concept is to generalise constrained density functional theory to free it from any assumptions about the spatial confinement of electrons and holes, but to maintain all the advantages of a variational method. The task of calculating the lowest excited state of a given symmetry is thereby simplified to one of performing a simple, low-cost sequence of coupled DFT calculations. We demonstrate the efficacy of the method by calculating the lowest single-particle singlet and triplet excitation energies in the well-known Thiel molecular test set, with results which are in good agreement with linear-response time-dependent density functional theory (LR-TDDFT). Furthermore, we show that XDFT can successfully capture two-electron excitations, in principle, offering a flexible approach to target specific effects beyond state-of-the-art adiabatic-kernel LR-TDDFT. Overall the method makes optical gaps and electron-hole binding energies readily accessible at a computational cost and scaling comparable to that of standard density functional theory. Owing to its multiple qualities beneficial to high-throughput studies where the optical gap is of particular interest; namely broad applicability, low computational demand, and ease of implementation and automation, XDFT presents as a viable candidate for research within materials discovery and informatics frameworks.

Edge superconductivity in multilayer WTe2 Josephson junction.

WTe2, as a type-II Weyl semimetal, has 2D Fermi arcs on the (001) surface in the bulk and 1D helical edge states in its monolayer. These features have recently attracted wide attention in condensed matter physics. However, in the intermediate regime between the bulk and monolayer, the edge states have not been resolved owing to its closed band gap which makes the bulk states dominant. Here, we report the signatures of the edge superconductivity by superconducting quantum interference measurements in multilayer WTe2 Josephson junctions and we directly map the localized supercurrent. In thick WTe2 ([Formula: see text], the supercurrent is uniformly distributed by bulk states with symmetric Josephson effect ([Formula: see text]). In thin WTe2 (10 nm), however, the supercurrent becomes confined to the edge and its width reaches up to [Formula: see text]and exhibits non-symmetric behavior [Formula: see text]. The ability to tune the edge domination by changing thickness and the edge superconductivity establishes WTe2 as a promising topological system with exotic quantum phases and a rich physics.

How do phonons relax molecular spins?

The coupling between electronic spins and lattice vibrations is fundamental for driving relaxation in magnetic materials. The debate over the nature of spin-phonon coupling dates back to the 1940s, but the role of spin-spin, spin-orbit, and hyperfine interactions has never been fully established. Here, we present a comprehensive study of the spin dynamics of a crystal of Vanadyl-based molecular qubits by means of first-order perturbation theory and first-principles calculations. We quantitatively determine the role of the Zeeman, hyperfine, and electronic spin dipolar interactions in the direct mechanism of spin relaxation. We show that, in a high magnetic field regime, the modulation of the Zeeman Hamiltonian by the intramolecular components of the acoustic phonons dominates the relaxation mechanism. In low fields, hyperfine coupling takes over, with the role of spin-spin dipolar interaction remaining the less important for the spin relaxation.

Synthesis of centimeter-size free-standing perovskite nanosheets from single-crystal lead bromide for optoelectronic devices.

Considerable attention has been drawn to the lead halide perovskites (LHPs) because of their outstanding optoelectronic characteristics. LHP nanosheets (NSs) grown from single crystalline lead halide possess advantages in device applications as they provide the possibility for control over morphology, composition, and crystallinity. Here, free-standing lead bromide (PbBr2) single-crystalline NSs with sizes up to one centimeter are synthesized from solution. These NSs can be converted to LHP while maintaining the NS morphology. We demonstrate that these perovskite NSs can be processed directly for fabrication of photodetector and laser arrays on a large scale. This strategy will allow high-yield synthesis of large-size perovskite NSs for functional devices in an integrated photonics platform.

Dirac-cone induced gating enhancement in single-molecule field-effect transistors.

Using graphene as electrodes provides an opportunity for fabricating stable single-molecule field-effect transistors (FETs) operating at room temperature. However, the role of the unique graphene band structure in charge transport of single-molecule devices is still not clear. Here we report the Dirac-cone induced electrostatic gating effects in single-molecule FETs with graphene electrodes and a solid-state local bottom gate. With the highest occupied molecular orbital (HOMO) as the dominating conduction channel and the graphene leads remaining intrinsic at zero gate voltage, electrostatic gating on the HOMO and the density of states of graphene at the negative gate polarity reinforces each other, resulting in an enhanced conductance modulation. In contrast, gating effects on the HOMO and the graphene states at the positive gate polarity are opposite. Depending on the gating efficiencies, the conductance can decrease, increase or remain almost unchanged when a more positive gate voltage is applied. Our observations can be well understood by a modified single-level model taking into account the linear dispersion of graphene near the Dirac point. Single-molecule FETs with Dirac-cone enhanced gating have shown high performances, with the modulation of a wide range of current over one order of magnitude. Our studies highlight the advantages of using graphene as an electrode material for molecular devices and pave the way for single-molecule FETs toward circuitry applications.