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Abstract Understanding structural flexibility of metal-organic frameworks (MOFs) via molecular dynamics simulations is crucial to design better MOFs. Density functional theory (DFT) and quantum-chemistry methods provide highly accurate molecular dynamics, but the computational overheads limit their use in long time-dependent simulations. In contrast, classical force fields struggle with the description of coordination bonds. Here we develop a DFT-accurate machine-learning spectral neighbor analysis potentials for two representative MOFs. Their structural and vibrational properties are then studied and tightly compared with available experimental data. Most importantly, we demonstrate an active-learning algorithm, based on mapping the relevant internal coordinates, which drastically reduces the number of training data to be computed at the DFT level. Thus, the workflow presented here appears as an efficient strategy for the study of flexible MOFs with DFT accuracy, but at a fraction of the DFT computational cost.

Abstract 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. The method is applied to distinct domains: the investigation of the morphology of printed graphene nanosheets networks, obtained via focused ion beam-scanning electron microscope (FIB-SEM), magnetic resonance imaging of the human brain, and X-ray computed tomography scans of the abdomen. The accuracy of the 3D tomographic maps can be quantified through computer-vision metrics, but most importantly with the precision on the physical quantities retrievable from the reconstructions, in the case of FIB-SEM the porosity, tortuosity, and effective diffusivity. This work showcases a versatile image-augmentation strategy for optimizing 3D tomography acquisition conditions, while preserving the information content.

Abstract Iron pentacarbonyl (IPC) gas forms upon the reaction of carbon monoxide with Fe containing metallic surfaces under gas reforming conditions. IPC formation can sometimes reach alarming levels that cause metal loss, pipeline thinning corrosion, catalyst poisoning, and contamination of sensitive industrial equipment. In this work, we demystify using multiscale computational modeling the mechanism of Iron pentacarbonyl formation: Density functional theory (DFT) is used to explore various catalytic reactions that involve a Fe adatom reacting with adsorbed carbon monoxide. Our calculated carbonyls desorption barriers on a perfect and clean Fe surface are too high to allow the carbonyls to form then desorb at temperatures

Abstract 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.

Abstract 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.

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.

Abstract Kohn–Sham density functional theory (KS-DFT) is a powerful method to obtain key materials’ properties, but the iterative solution of the KS equations is a numerically intensive task, which limits its application to complex systems. To address this issue, machine learning (ML) models can be used as surrogates to find the ground-state charge density and reduce the computational overheads. We develop a grid-centred structural representation, based on Jacobi and Legendre polynomials combined with a linear regression, to accurately learn the converged DFT charge density. This integrates into a ML pipeline that can return any density-dependent observable, including energy and forces, at the quality of a converged DFT calculation, but at a fraction of the computational cost. Fast scanning of energy landscapes and producing starting densities for the DFT self-consistent cycle are among the applications of our scheme.

Abstract Intrinsic one-dimensional (1D) ferroelectric materials are rarely reported but are highly sought to break the size limit of nanostructured conventional ferroelectrics. Herein, we report a class of inborn 1D ferroelectric nanowires, namely 1D NbOX3 (X = Cl, Br, and I), that can be directly obtained from experimentally realized van der Waals crystals. In addition to the sizable spontaneous polarization, 1D NbOX3 exhibits low ferroelectric switching barriers, small coercive electric fields, and high critical temperature, governed by the hybridization of the Nb empty d orbitals and the O p orbitals (d 0 rule). Moreover, the double-channel structure of 1D NbOX3 also enables the emergence of 1D antiferroelectric metastable states. Our findings not only propose a class of 1D ferroelectric materials toward the development of miniaturized and high-density electronic devices, but also pave an avenue of obtaining intrinsic 1D ferroelectrics from van der Waals crystals.

Abstract Vertically stacked van der Waals heterostructures made of two-dimensional compounds are almost an infinite playground for the fabrication of nano-engineered materials for the most diverse applications. Unfortunately, high-throughput electronic structure theory, which often serves as a guidance for material design, is not practical in this case. In fact, the compositional and structural complexity of van der Waals heterostructures make the number of prototypes to calculate combinatorially large. In this work a method is developed to compute the bandstructure of van der Waals heterostructures with an arbitrary composition and geometry using minimal computational resources. Such scheme is applied to the systematic study of a library of two-dimensional hexagonal X Y 2 compounds. The method is based on the density functional theory and on the assumption that the inter-layer electronic interaction is limited to classical electrostatic screening. Our analysis enables us to identify and categorize a large range of van der Waals bilayer heterostructures with electronic band gaps of different nature ranging from 0.1 to 5.5 eV and various types of band line-up.

High-resolution electron microscopy is achievable only when a high electron dose is employed, a practice that may cause damage to the specimen and, in general, affects the observation. This drawback sets some limitations on the range of applications of high-resolution electron microscopy. Our work proposes a strategy, based on machine learning, which enables a significant improvement in the quality of Scanning Transmission Electron Microscope images generated at low electron dose, strongly affected by Poisson noise. In particular, we develop an autoencoder, trained on a large database of images, which is thoroughly tested on both synthetic and actual microscopy data. The algorithm is demonstrated to drastically reduce the noise level and approach ground-truth precision over a broad range of electron beam intensities. Importantly, it does not require human data pre-processing or the explicit knowledge of the dose level employed and can run at a speed compatible with live data acquisition. Furthermore, a quantitative unbiased benchmarking protocol is proposed to compare different denoising workflows.

A new type of cyclotron orbit combining surface Fermi arcs and bulk states in topological semimetals has recently been proposed as Weyl orbit. Here, Zhang et al. report the evolution of Shubnikov-de Haas oscillations in Dirac semimetal Cd3As2 nanoplates along with a quantum Hall state possibly arising from such Weyl orbit.

TiS2 oxidation: moisture is the critical element preventing environmental stability The degradation dynamics of TiS2 reveal that the flakes oxidise in water and atmosphere, pointing towards moisture as the key driving force. A team led by Valeria Nicolosi at Trinity College Dublin used advanced electron microscopy techniques to investigate the influence of different environments on the deterioration pathways of TiS2, a promising candidate for future energy storage applications. By comparing the effect of ex-situ oxidation by water and oxygen at room temperature, and in-situ oxidation at high temperatures, water was proven to effectively oxidise the TiS2 flakes from the edges thereby forming an amorphous oxide phase. Conversely, the degradation was found to proceed more slowly in atmosphere or vacuum conditions. These results suggest that TiS2 oxidation could be avoided in a desiccated environment that would prevent water molecules from dissociating in reactive ionic species.

Single molecule magnets exhibit faster spin relaxation rates than expected from models based on tunnelling through the relaxation barrier. Here, the authors show, using first principles calculations, that anharmonic spin-phonon interactions may explain the under-barrier spin relaxation.

At the hybrid interface between an organic molecular layer and a metallic magnetic surface, spin-filtering effects may be exploited for the generation of spin polarization. Here, the authors demonstrate a dynamic spin-filtering effect across the Co/Alq3 interface, mediated via a second Alq3layer.

It has been predicted that the presence of strong electronic correlations may generate new phases in materials with topologically non-trivial band structure. Here, the authors demonstrate the generation of Dirac mass in the correlated Dirac semimetal candidate ZrTe5under high magnetic fields.

We demonstrate single atom anisotropic magnetoresistance on the surface of a topological insulator, arising from the interplay between the helical spin–momentum-locked surface electronic structure and the hybridization of the magnetic adatom states. Our first-principles quantum transport calculations based on density functional theory for Mn on Bi _2 Se _3 elucidate the underlying mechanism. We complement our findings with a two dimensional model valid for both single adatoms and magnetic clusters, which leads to a proposed device setup for experimental realization. Our results provide an explanation for the conflicting scattering experiments on magnetic adatoms on topological insulator surfaces, and reveal the real space spin texture around the magnetic impurity.

We have studied the spin-dependent electronic properties of the interface formed between epitaxial Co thin films deposited on Cu(001) and the experimental molecule tris-(9-oxidophenalenone)-aluminum ^(III) (Al(OP) _3 ), created as a variation of the prototypical organic semiconductor Alq _3 to tailor the spin filtering properties by modifying chemisorption with cobalt. The interfaces have been grown under ultra-high vacuum conditions by progressive deposition of 0.5–5 nm Al(OP) _3 on the freshly prepared cobalt substrate. For every growth step we have monitored the energy level alignment at the interface as well as the spin polarization of the occupied manifold by spin-resolved photoemission spectroscopy. We identify two hybrid interface states in the energy window of 2 eV below the Fermi energy. The first is at 0.9 eV below E _F and shows an 8% higher spin polarization than Co, while the second is at 1.6 eV below E _F and shows a spin polarization reduced by 4%.