Video frame interpolation neural network for 3D tomography across different length scales.

Three-dimensional (3D) tomography is a powerful investigative tool for many scientific domains, going from materials science, to engineering, to medicine. Many factors may limit the 3D resolution, often spatially anisotropic, compromising the precision of the information retrievable. A neural network, designed for video-frame interpolation, is employed to enhance tomographic images, achieving cubic-voxel resolution.

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

We examine the coherent spin-dependent transport properties of the van der Waals (vdW) ferromagnet FeGeTe 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.

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.

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 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.

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.

Stiffness and Atomic-Scale Friction in Superlubricant MoS Bilayers.

Molecular dynamics simulations, performed with chemically accurate 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.

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 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 symmetry are transmitted with large probability, resulting in a potentially large tunneling magnetoresistance (TMR) ratio.

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.

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.

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'-CrCoS, are demonstrated to display room-temperature triferroicity.

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.

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 condensation reactions of the central molecules terminated with -phenylenediamines with -quinone-functionalized nanogaps of graphene and SWCNT electrodes.

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 MnPSe [ 2021, 16 (7), 782] and guided by a heteromagnetic structural design, we engineer strong magnetoelectric coupling in a hydrogen-intercalated 2D MnPSe bilayer. Hydrogen functionalization breaks the centrosymmetry of bilayer MnPSe, leading to out-of-plane ferroelectricity.

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.

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.

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

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 Co 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.

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.

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.

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.

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 (PbBr) single-crystalline NSs with sizes up to one centimeter are synthesized from solution.

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 ABC chalcogenide (I-V-VI) and halide (I-II-VII) perovskites.

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.