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Abstract We use elastic and inelastic neutron scattering (INS) to study the antiferromagnetic (AF) phase transitions and spin excitations in the two-dimensional (2D) zig-zag antiferromagnet FePSe3. By determining the magnetic order parameter across the AF phase transition, we conclude that the AF phase transition in FePSe3 is first-order in nature. In addition, our INS measurements reveal that the spin waves in the AF ordered state have a large easy-axis magnetic anisotropy gap, consistent with an Ising Hamiltonian, and possible biquadratic magnetic exchange interactions. On warming across T N, we find that dispersive spin excitations associated with three-fold rotational symmetric AF fluctuations change into FM spin fluctuations above T N. These results suggest that the first-order AF phase transition in FePSe3 may arise from the competition between C 3 symmetric AF and C 1 symmetric FM spin fluctuations around T N, in place of a conventional second-order AF phase transition.

Abstract Electrons in metals can show a giant anomalous Hall effect (AHE) when interacting with characteristic spin texture. The AHE has been discussed in terms of scalar-spin-chirality (SSC) in long-range-ordered noncollinear spin textures typified by Skyrmion. The SSC becomes effective even in the paramagnetic state with thermal fluctuations, but the resultant AHE has been limited to be very small. Here, we report the observation of large AHE caused by the spin fluctuation near the devil’s staircase transition in a collinear antiferromagnetic metal SrCo6O11. The AHE is prominent near and above the transition temperature at moderate magnetic fields, where the anomalous Hall angle becomes the highest level among known oxide collinear ferromagnets/antiferromagnets (>2%). Furthermore, the anomalous Hall conductivity is quadratically scaled to the conductivity. These results imply that the thermally induced solitonic spin defects inherent to the devil’s staircase transition promote SSC-induced skew scattering.

Abstract Mixed-dimensional van der Waals (vdW) heterostructures offer promising platforms for exploring interesting phenomena and functionalities. To exploit their full potential, precise epitaxial processes and well-defined heterointerfaces between different components are essential. Here, we control the growth of one-dimensional (1D) vdW microwires on hexagonal crystals via plasma treatment of the growth templates. AgCN serves as a model 1D system for examining the dependence of the nucleation and growth parameters on the surface treatment conditions and substrate types. The oxygen-plasma-treated transition metal dichalcogenides form step edges mediated by formation of surface metal oxides, leading to robust AgCN epitaxy with an enhanced nucleation density and low horizontal growth rates. Monte Carlo simulations reproduce the experimentally observed growth behaviors and unveil the crucial growth parameters, such as surface diffusivity. The plasma treatment results in distinct effects on graphite and hexagonal boron nitride templates, which undergo plasma-induced amorphization and deactivation of the AgCN vdW epitaxy. We achieve the selective growth of AgCN microwires on graphite using the deactivated vdW epitaxy. This study offers significant insights into the impact of surface treatment on 1D vdW epitaxy, opening avenues for controlled fabrication of mixed-dimensional vdW heterostructures.

Abstract The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. However, current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex constraints. Here, we devise an Evolution-Guided Bayesian Optimization (EGBO) algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement (qNEHVI) optimizer; this not only solves for the Pareto Front (PF) efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space. The algorithm is developed together with a custom self-driving lab for seed-mediated silver nanoparticle synthesis, targeting 3 objectives (1) optical properties, (2) fast reaction, and (3) minimal seed usage alongside complex constraints. We demonstrate that, with appropriate constraint handling, EGBO performance improves upon state-of-the-art qNEHVI. Furthermore, across various synthetic multi-objective problems, EGBO shows significative hypervolume improvement, revealing the synergy between selection pressure and the qNEHVI optimizer. We also demonstrate EGBO’s good coverage of the PF as well as comparatively better ability to propose feasible solutions. We thus propose EGBO as a general framework for efficiently solving constrained multi-objective problems in high-throughput experimentation platforms.

Abstract Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties. Pulsed laser deposition (PLD) is emerging as a viable autonomous synthesis tool, and so the need arises to develop machine learning (ML) techniques that are capable of extracting information from in situ diagnostics. Here, we demonstrate that intensified-CCD image sequences of the plasma plume generated during PLD can be used for anomaly detection and the prediction of thin film growth kinetics. We develop multi-output (2 + 1)D convolutional neural network regression models that extract deep features from plume dynamics that not only correlate with the measured chamber pressure and incident laser energy, but more importantly, predict parameters of an auto-catalytic film growth model derived from in situ laser reflectivity experiments. Our results demonstrate how ML with in situ plume diagnostics data in PLD can be utilized to maintain deposition conditions in an optimal regime. Further, the predictive capabilities of plume dynamics on the kinetics of film growth or other film properties prior to deposition provides a means for rapid pre-screening of growth conditions for the non-expert, which promises to accelerate materials optimization with PLD.

Abstract Direct electron detectors in scanning transmission electron microscopy give unprecedented possibilities for structure analysis at the nanoscale. In electronic and quantum materials, this new capability gives access to, for example, emergent chiral structures and symmetry-breaking distortions that underpin functional properties. Quantifying nanoscale structural features with statistical significance, however, is complicated by the subtleties of dynamic diffraction and coexisting contrast mechanisms, which often results in a low signal-to-noise ratio and the superposition of multiple signals that are challenging to deconvolute. Here we apply scanning electron diffraction to explore local polar distortions in the uniaxial ferroelectric Er(Mn,Ti)O3. Using a custom-designed convolutional autoencoder with bespoke regularization, we demonstrate that subtle variations in the scattering signatures of ferroelectric domains, domain walls, and vortex textures can readily be disentangled with statistical significance and separated from extrinsic contributions due to, e.g., variations in specimen thickness or bending. The work demonstrates a pathway to quantitatively measure symmetry-breaking distortions across large areas, mapping structural changes at interfaces and topological structures with nanoscale spatial resolution.

Abstract High-entropy alloys (HEAs) have attracted extensive attention in recent decades due to their unique chemical, physical, and mechanical properties. An in-depth understanding of the structure–property relationship in HEAs is the key to the discovery and design of new compositions with desirable properties. Related to this, materials genome strategy has been increasingly used for discovering new HEAs with better performance. This review paper provides an overview of key advances in this fast-growing area, along with current challenges and potential opportunities for HEAs. We also discuss related topics, such as high-throughput preparation, characterization, and computation of HEAs, and data-driven machine learning for accelerating alloy development. Finally, future research directions and perspectives for the materials genome-assisted design of HEAs are proposed and discussed.

Abstract Wearable optical sensors have emerged as a promising technology, opening up a new way to monitor human sweat. With the advancement of integrated optical devices, optical materials, and structure design, the current optical skin interfaces primarily employ four analytical methods to transmit sweat chemical information into optical signals: colorimetry, surface-enhanced Raman spectroscopy, fluorescence, and electrochemiluminescence. To improve portability, many external laser source devices and imaging modules are upgraded based on different optical methods. Here, we summarize recent progress in optical sweat sensors, focusing on their principles, development, advantages, and limitations. Finally, current challenges and future prospects of wearable optical sensors in materials, sweat collection, data analysis, and external integrated electronics are discussed.

Abstract The electrochemical performance of all-solid-state batteries needs to be improved by addressing the poor stability against the lithium metal anode and the high interfacial resistance at the cathode–solid electrolyte interface. Here, metal halide-doped Li7P2S8I–type (LPSI) solid electrolytes are synthesized that improve the electrochemical performance of all-solid-state batteries. The solid electrolytes exhibit a higher ionic conductivity value of 7.77 mS cm−1 than bare LPSI solid electrolytes of 3.96 mS cm−1, at room temperature. The metal halide-doped LPSI solid electrolyte is also stable against the lithium metal anode, with a calculated critical current density value of 1 mA cm−2. The fabricated all-solid-state battery shows high electrochemical performance with 99.2% specific capacity retention after 250 cycles at a 0.5 C rate. The results of post galvanostatic charge–discharge analysis confirms that the proposed metal halide-doped LPSI solid electrolyte exhibits improved interfacial stability compared to bare LPSI solid electrolytes.

Abstract Adhesive polymer films with anisotropic properties on either side have attracted tremendous interest for biomedical and engineering applications. However, developing an innovative solution that provides effective adhesion under both dry and wet conditions remains a considerable challenge. In this study, we devised a novel process for creating adhesive films by casting polydimethylsiloxane (PDMS) onto a thermoplastic polyurethane (TPU) substrate. During the curing process, the PDMS layer in contact with the TPU was lightly cross-linked, which significantly increased adhesion. The catalytic reaction used for polymerization was regulated by the TPU, which is known to adsorb metal ions. This adhesive PDMS film (APF) demonstrated outstanding adhesion on various substrates under dry and underwater conditions and maintained adhesion even after repeated use. In practical applications, the APF proved to be an effective waterproof patch by adhering to the surfaces of objects submerged in water.

Abstract Short-time oxidation behavior of nanocrystalline Ta coating is studied at 850 °C in comparison with that of the Ta sheet. Owing to the large PBR value and insufficient expansion space, the oxide scale on Ta sheet is dramatically cracked, delaminated and pulverized, resulting in rapid deterioration. For nanocrystalline Ta coatings with columnar structures and quantitative grain boundaries, a rapid oxygen diffusion rate causes no initial Ta2O5 to form. The gap between columns provides spaces for bulk expansion, resulting in few opening cracks and delamination. Ta oxidation experiences a crystallization course from amorphous Ta oxide, leading to in situ temperature surging and thus pulverization.

Abstract The corrosion performance of 6xxx series Al alloys has been found to depend on small changes in composition and microstructure. The corrosion behaviors of three aluminum alloys, AA6111, AA6451, and AA6016, were investigated. AA6111, containing primarily α (Al15 (Fe,Mn)3Si2) intermetallic particles (IMPs), and AA6016, containing primarily β (Al8Fe2Si) IMPs, exhibited the best and the worst overall corrosion performances, respectively, as indicated by the extent of corrosion in exposure tests. However, this ranking was not predicted by the standard interpretation of potentiodynamic polarization curves measured on the alloys. The corrosion susceptibilities of the three alloys were further investigated by evaluating the electrochemical behavior of the component phases separately. Bulk analogs of the component phases were fabricated using standard alloy casting techniques. The fabricated bulk analogs of α and β IMPs, as well as the three alloy matrix phases, were tested using either macrocell or microcell testing. An explanation for the alloy performances was developed by combining the behavior of the component phases.

Abstract With the popularity of smart terminals, wearable electronic devices have shown great market prospects, especially high-sensitivity pressure sensors, which can monitor micro-stimuli and high-precision dynamic external stimuli, and will have an important impact on future functional development. Compressible flexible sensors have attracted wide attention due to their simple sensing mechanism and the advantages of light weight and convenience. Sensors with high sensitivity are very sensitive to pressure and can detect resistance/current changes under pressure, which has been widely studied. On this basis, this review focuses on analyzing the performance impact of device structure design strategies on high sensitivity pressure sensors. The design of structures can be divided into interface microstructures and three-dimensional framework structures. The preparation methods of various structures are introduced in detail, and the current research status and future development challenges are summarized.

Abstract In this study, a simple route for the synthesis of hierarchical W18O49 assembled by nanowires is reported. The morphologies and formation of W18O49 single-crystal could be controlled by changing the concentration of WCl6-ethanol solution. This synthesis strategy has the advantages that the hierarchical W18O49 microspheres could be economic synthesized at 180 °C without adding additives. Furthermore, efficient optical absorption properties in ultraviolet, visible and near-infrared region were obtained for the hierarchical W18O49 microspheres comparing with nanowires. These results will further promote the research of tungsten-based oxide nanomaterials. Graphical abstract

Abstract The increasing demand for energy, depletion of traditional energy sources, and environmental concerns have generated an energy crisis in recent years. To address this challenge, innovative and low-cost solutions have been sought, such as the use of 3D printing for decentralized hydrogen production. In this paper, we present the production of conductive filaments prepared from polylactic acid (PLA) and Carbon Black (CB), which were used for the 3D printing of electrodes. The produced materials were characterized by scanning electron microscopy, thermal analysis, and electrochemical techniques. The 3D-printed electrodes were used as substrates for CoPi electrodeposition to evaluate their performance in hydrogen production. The 3D-printed electrodes, made from filaments prepared in the laboratory, demonstrated superior electrochemical performance and hydrogen production compared to electrodes printed compared with commercial filament. The PLA@CB72R and CoPi#PLA@CB72R electrodes showed better hydrogen production performance, 10.08 and 10.20 μmol min−1, respectively. This study provides a perspective for the manufacture of filaments and 3D-printed electrodes for energy conversion applications, such as hydrogen production.

We have developed a novel rheo-optical measurement system based on two-dimensional polarization analysis, which can evaluate the rheological properties and structure of a complex fluid simultaneously. To assess the utility of the system, we used it to investigate the relationship between yield behavior and structural evolution in a TEMPO-oxidized cellulose nanofiber (T-CNF) suspension, which is a yield-stress fluid that has been actively studied in recent years. To analyze the structural evolution of a T-CNF suspension, stress-ramp tests were conducted. A two-step yield behavior was observed, and distributions of retardation and orientation axis varied dramatically with increasing shear stress. In particular, different distributions were observed in the three regions: after the first yield point, before the second yield point, and after the second yield point. In experiments with a low-concentration T-CNF suspension that exhibits no yield behavior, the retardation increased monotonically with increasing shear stress, and its distribution was uniform. It was demonstrated that the yield behavior and related structures can be analyzed from these results. More detailed structural mechanisms require various rheological tests using the developed system. However, the present insights demonstrate the valuable information provided by the developed rheo-optical measurement system, contributing essential knowledge for applications in various fields.

To investigate the impact of curing environments on the mechanical properties of coal gangue cementation (CGC), various curing methods were established, including standard bag curing, standard curing, natural sealing curing, natural curing, water curing, and varying curing ages. By examining the uniaxial compressive strength (UCS) and stress–strain relationship of CGC by applying axial loads, the influence mechanism was analyzed in terms of both physical and chemical reactions. Furthermore, a mechanistic structural model was established to illustrate the impact of the curing environment on the mechanical properties of CGC. The primary substances and reasons affecting the mechanical properties of CGC were analyzed through the use of scanning electron microscopy and X-ray diffraction techniques. Evaluation of influence factors on CGC mechanical properties by grey correlation degree. The findings indicate that curing temperature, humidity, and carbonization are the principal factors influencing the UCS. Maintaining constant temperature and humidity while isolating CO2 is conducive to improving the UCS. The hydration products, such as needle-like ettringite and white fibrous calcium silicate hydrogel, fill the internal voids of CGC and are the primary substances affecting UCS. The hydration products formed during standard curing and natural curing of CGC can undergo carbonation with CO2 to form CaCO3, which interacts with ettringite and hydrated calcium silicate to provide strength support for CGC. However, beyond a certain age, CO2 will progressively diminish the UCS; the larger the contact area and the longer the exposure time to the gel materials in CGC, the faster the UCS decreases.

Abstract Intensive research has been carried out on two-dimensional materials, in particular molybdenum disulfide, towards high-performance field effect transistors for integrated circuits1. Fabricating transistors with ohmic contacts is a challenging task due to the formation of a high Schottky barrier that severely limits the performance of the transistors for real-world applications. Graphene-based heterostructures can be used in addition to, or as a substitute for unsuitable metals. In this paper, we present lateral heterostructure transistors made of scalable chemical vapor-deposited molybdenum disulfide and chemical vapor-deposited graphene achieving a low contact resistances of about 9 kΩ·µm and high on/off current ratios of 108. Furthermore, we also present a theoretical model calibrated on our experiments showing further potential for scaling transistors and contact areas into the few nanometers range and the possibility of a substantial performance enhancement by means of layer optimizations that would make transistors promising for use in future logic integrated circuits.

Abstract Strain- and defect-engineering are two powerful approaches to tailor the opto-electronic properties of two-dimensional (2D) materials, but the relationship between applied mechanical strain and behavior of defects in these systems remains elusive. Using first-principles calculations, we study the response to external strain of h-BN, graphene, MoSe2, and phosphorene, four archetypal 2D materials, which contain substitutional impurities. We find that the formation energy of the defect structures can either increase or decrease with bi-axial strain, tensile or compressive, depending on the atomic radius of the impurity atom, which can be larger or smaller than that of the host atom. Analysis of the strain maps indicates that this behavior is associated with the compressive or tensile local strains produced by the impurities that interfere with the external strain. We further show that the change in the defect formation energy is related to the change in elastic moduli of the 2D materials upon introduction of impurity, which can correspondingly increase or decrease. The discovered trends are consistent across all studied 2D materials and are likely to be general. Our findings open up opportunities for combined strain- and defect-engineering to tailor the opto-electronic properties of 2D materials, and specifically, the location and properties of single-photon emitters.

Abstract Graphene/hBN/graphene tunnel devices offer promise as sensitive mid-infrared photodetectors but the microscopic origin underlying the photoresponse in them remains elusive. In this work, we investigated the photocurrent generation in graphene/hBN/graphene tunnel structures with localized defect states under mid-IR illumination. We demonstrate that the photocurrent in these devices is proportional to the second derivative of the tunnel current with respect to the bias voltage, peaking during tunneling through the hBN impurity level. We revealed that the origin of the photocurrent generation lies in the change of the tunneling probability upon radiation-induced electron heating in graphene layers, in agreement with the theoretical model that we developed. Finally, we show that at a finite bias voltage, the photocurrent is proportional to either of the graphene layers heating under the illumination, while at zero bias, it is proportional to the heating difference. Thus, the photocurrent in such devices can be used for accurate measurements of the electronic temperature, providing a convenient alternative to Johnson noise thermometry.

Abstract The intermediate-range order of covalently bonded glasses has been extensively studied in terms of their diffraction peaks observed at low scattering angles; these peaks are called the first sharp diffraction peaks (FSDPs). Although the atomic density fluctuations originating from the quasilattice planes are a critical scientific target, direct experimental observations of these fluctuations are still lacking. Here, we report the direct observation of the atomic density fluctuations in silica glass by energy-filtered angstrom-beam electron diffraction. The correspondence between the local electron diffraction patterns of FSDPs and the atomic configurations constructed based on the X-ray and neutron diffraction results revealed that the local atomic density fluctuations originated from the quasi-periodic alternating arrangements of the columnar chain-like atomic configurations and interstitial tubular voids, as in crystals. We also discovered longer-range fluctuations associated with the shoulder of the FSDP on the low-Q side. The hierarchical fluctuations inherent in materials could aid in the elucidation of their properties and performance.

Abstract The multifaceted physics of oxides is shaped by their composition and the presence of defects, which are often accompanied by the formation of polarons. The simultaneous presence of polarons and defects, and their complex interactions, pose challenges for first-principles simulations and experimental techniques. In this study, we leverage machine learning and a first-principles database to analyze the distribution of surface oxygen vacancies (VO) and induced small polarons on rutile TiO2(110), effectively disentangling the interactions between polarons and defects. By combining neural-network supervised learning and simulated annealing, we elucidate the inhomogeneous VO distribution observed in scanning probe microscopy (SPM). Our approach allows us to understand and predict defective surface patterns at enhanced length scales, identifying the specific role of individual types of defects. Specifically, surface-polaron-stabilizing VO-configurations are identified, which could have consequences for surface reactivity.

Abstract The prediction of configurational disorder properties, such as configurational entropy and order-disorder phase transition temperature, of compound materials relies on efficient and accurate evaluations of configurational energies. Previous cluster expansion methods are not applicable to configurationally-complex material systems, including those with atomic distortions and long-range orders. In this work, we propose to leverage the versatile expressive capabilities of graph neural networks (GNNs) for efficient evaluations of configurational energies and present a workflow combining attention-based GNNs and Monte Carlo simulations to calculate the disorder properties. Using the dataset of face-centered tetragonal gold copper without and with local atomic distortions as an example, we demonstrate that the proposed data-driven framework enables the prediction of phase transition temperatures close to experimental values. We also elucidate that the variance of the energy deviations among configurations controls the prediction accuracy of disorder properties and can be used as the target loss function when training and selecting the GNN models. The work serves as a fundamental step toward a data-driven paradigm for the accelerated design of configurationally-complex functional material systems.

Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/

Abstract Machine learning algorithms have shown great accuracy in predicting chemical reaction outcomes and retrosyntheses. However, designing synthesis pathways remains challenging for existing machine learning models which are trained for single-step prediction. In this manuscript, we propose to recast the retrosynthesis problem as a string optimization problem in a data-driven fingerprint space, leveraging the similarity between chemical reactions and embedding vectors. Based on this premise, multi-step complex synthesis can be conceptualized as sequences that link multidimensional vectors (fingerprints) representing individual chemical reaction steps. We extracted an extensive corpus of chemical synthesis from patents and converted them into multidimensional strings. While optimizing the retrosynthetic path, we use the Euclidean metric to minimize the distance between the expanded trajectory of the growing retrosynthesis string and the corpus of extracted strings. By doing so, we promote the assembly of synthetic pathways that, in the chemical reaction space, will be more similar to existing retrosyntheses, thereby inheriting the strategic guidelines designed by human experts. We integrated this approach into the RXN platform ( https://rxn.res.ibm.com/ ) and present the method’s application to complex synthesis as well as its ability to produce better synthetic strategies than current methodologies.

Abstract The emergence of magnetic transition metal dichalcogenides has significantly advanced the development of valleytronics due to the spontaneous breaking of time-reversal symmetry and space-inversion symmetry. However, the lack of regulation methods has prevented researchers from exploring their potential applications. Herein, we propose to use strain engineering to control the spin-valley coupling in the sliding ferroelectric bilayer 2H-VX2 (X = S, Se, Te). Four multiferroic states are constructed by combining the sliding ferroelectricity and antiferromagnetism in the R-stacking bilayer VX2, where the spin and valley polarizations are coupled together from the layer-dependent spin-polarized band structures. By applying a small external strain or pressure on the out-of-plane van der Waals direction, we predicted that there is an antiferromagnetic to magnetic transition in the bilayer VX2, leading to the interesting spin-polarized and chiral circularly polarized radiation at K+ and K- valleys, similar to those found in the magnetic monolayer. To comprehend the coupling between various degrees of freedom in these multiferroic systems, we have developed an effective k·p model. This model unveils a linear relationship between the electric polarization generated by interlayer sliding and the energy difference of the valence band maximum at K+ and K- valleys. Thus, providing an alternate method to measure the electric polarization in the sliding ferroelectrics. Based on the strong coupling between the strain, spin-valley, and electric polarization, it is likely to use the strain to control the interesting emerging properties of 2H-VX2 such as the anomalous valley Hall effect.

Abstract The vast compositional and configurational spaces of multi-element metal halide perovskites (MHPs) result in significant challenges when designing MHPs with promising stability and optoelectronic properties. In this paper, we propose a framework for the design of B-site-alloyed ABX3 MHPs by combining density functional theory (DFT) and machine learning (ML). We performed generalized gradient approximation with Perdew–Burke–Ernzerhof functional for solids (PBEsol) on 3,159 B-site-alloyed perovskite structures using a compositional step of 1/4. Crystal graph convolution neural networks (CGCNNs) were trained on the 3159 DFT datasets to predict the decomposition energy, bandgap, and types of bandgaps. The trained CGCNN models were used to explore the compositional and configurational spaces of 41,400 B-site-alloyed ABX3 MHPs with a compositional step of 1/16, by accessing all possible configurations for each composition. The electronic band structures of the selected compounds were calculated using the hybrid functional (PBE0). Then, we calculated the optical absorption spectra and spectroscopic limited maximum efficiency of the selected compounds. Based on the DFT/ML-combined screening, 10 promising compounds with optimal bandgaps were selected, and from among these 10 compounds, CsGe0.3125Sn0.6875I3 and CsGe0.0625Pb0.3125Sn0.625Br3 were suggested as photon absorbers for single-junction and tandem solar cells, respectively. The design framework presented herein is a good starting point for the design of mixed MHPs for optoelectronic applications.

Abstract Machine learning-assisted modeling of the inter-atomic potential energy surface (PES) is revolutionizing the field of molecular simulation. With the accumulation of high-quality electronic structure data, a model that can be pretrained on all available data and finetuned on downstream tasks with a small additional effort would bring the field to a new stage. Here we propose DPA-1, a Deep Potential model with a gated attention mechanism, which is highly effective for representing the conformation and chemical spaces of atomic systems and learning the PES. We tested DPA-1 on a number of systems and observed superior performance compared with existing benchmarks. When pretrained on large-scale datasets containing 56 elements, DPA-1 can be successfully applied to various downstream tasks with a great improvement of sample efficiency. Surprisingly, for different elements, the learned type embedding parameters form a s p i r a l in the latent space and have a natural correspondence with their positions on the periodic table, showing interesting interpretability of the pretrained DPA-1 model.

Abstract A key strategy for designing environmental barrier coatings is to incorporate multiple rare-earth (RE) components into β- and γ-RE2Si2O7 to achieve multifunctional performance optimization. However, the polymorphic phase presents significant challenges for the design of multicomponent RE disilicates. Here, employing decision fusion, a machine learning (ML) method is crafted to identify multicomponent RE disilicates, showcasing notable accuracy in prediction. The well-trained ML models evaluated the phase formation capability of 117 (RE10.25RE20.25Yb0.25Lu0.25)2Si2O7 and (RE11/6RE21/6RE31/6Gd1/6Yb1/6Lu1/6)2Si2O7, which are unreported in experiments and validated by first-principles calculations. Utilizing model visualization, essential factors governing the formation of (RE10.25RE20.25Yb0.25Lu0.25)2Si2O7 are pinpointed, including the average radius of RE3+ and variations in different RE3+ combinations. On the other hand, (RE11/6RE21/6RE31/6Gd1/6Yb1/6Lu1/6)2Si2O7 must take into account the average mass and the electronegativity deviation of RE3+. This work combines material-oriented ML methods with formation mechanisms of multicomponent RE disilicates, enabling the efficient design of superior materials with exceptional properties for the application of environmental barrier coatings.

Abstract The exploration of multiferroic materials and their interaction with light at the nanoscale presents a captivating frontier in materials science. Bismuth Ferrite (BiFeO3, BFO), a standout among these materials, exhibits room-temperature ferroelectric and antiferromagnetic behaviour and magnetoelectric coupling. Of particular interest is the phenomenon of photostriction, the light-induced deformation of crystal structures, which enhances the prospect for device functionality based on these materials. Understanding and harnessing multiferroic phenomena holds significant promise in various technological applications, from optoelectronics to energy storage. The orientation of the ferroelectric axis is an important design parameter for devices formed from multiferroic materials. Determining its orientation in the laboratory frame of reference usually requires knowing multiple wavevector transfer (Q-Vector) directions, which can be challenging to establish due to the need for extensive reciprocal-space searches. Our study demonstrates a method to identify the ferroelectric axis orientation using Bragg Coherent X-ray Diffraction Imaging (BCDI) measurements at a single Q-vector direction. This method involves applying photostriction-inducing laser illumination across various laser polarisations. Our findings reveal that photostriction primarily occurs as a surface phenomenon at the nanoscale. Moreover, a photo-induced crystal length change ranging from 30 to 60 nm was observed, consistent with earlier findings on bulk material.

Abstract The versatile Bell-Evans-Polanyi (BEP) relation stipulates the kinetics of a reaction in terms of thermodynamics. Herein, we establish the BEP relation for the hydrogen evolution reaction (HER) from fundamental electrochemical principles leveraging the Butler-Volmer relation for a one-step, one-electron process and the transition state theory. Based on first-principles investigations of HER mechanisms on fourteen metal electrodes, we firmly justify the BEP relation solely using an easy-to compute hydrogen adsorption free energy and universal electrochemical constants.

Abstract Acquiring reliable microstructure datasets is a pivotal step toward the systematic design of materials with the aid of integrated computational materials engineering (ICME) approaches. However, obtaining three-dimensional (3D) microstructure datasets is often challenging due to high experimental costs or technical limitations, while acquiring two-dimensional (2D) micrographs is comparatively easier. To deal with this issue, this study proposes a novel framework called ‘Micro3Diff’ for 2D-to-3D reconstruction of microstructures using diffusion-based generative models (DGMs). Specifically, this approach solely requires pre-trained DGMs for the generation of 2D samples, and dimensionality expansion (2D-to-3D) takes place only during the generation process (i.e., reverse diffusion process). The proposed framework incorporates a concept referred to as ‘multi-plane denoising diffusion’, which transforms noisy samples (i.e., latent variables) from different planes into the data structure while maintaining spatial connectivity in 3D space. Furthermore, a harmonized sampling process is developed to address possible deviations from the reverse Markov chain of DGMs during the dimensionality expansion. Combined, we demonstrate the feasibility of Micro3Diff in reconstructing 3D samples with connected slices that maintain morphologically equivalence to the original 2D images. To validate the performance of Micro3Diff, various types of microstructures (synthetic or experimentally observed) are reconstructed, and the quality of the generated samples is assessed both qualitatively and quantitatively. The successful reconstruction outcomes inspire the potential utilization of Micro3Diff in upcoming ICME applications while achieving a breakthrough in comprehending and manipulating the latent space of DGMs.

Abstract The AV3Sb5 prototype kagome materials have been demonstrated as a versatile platform for exploring exotic properties in condensed matter physics, including charge density waves, superconductivity, non-trivial electron topology, as well as topological superconductivity. Here we identify that ANb3Bi5 (A = K, Rb, Cs) exhibit non-trivial coexisting superconductivity and topological properties via first-principles calculations. The negative formation energy and the absence of imaginary phonon dispersion demonstrate both thermodynamics and dynamics stabilities of ANb3Bi5 (A = K, Rb, Cs) under ambient conditions. By analytically solving the Allen-Dynes-modified McMillan formula, the superconducting transition temperatures are predicted to be 2.11, 2.15 and 2.21 K for KNb3Bi5, RbNb3Bi5, and CsNb3Bi5, respectively. More importantly, the kagome materials proposed here can be classified into $${{\mathbb{Z}}}_{2}$$ Z 2 topological metals due to the non-trivial topological index and the obvious surface states around the Fermi level. Such coexistence of superconductivity and non-trivial band characters in ANb3Bi5 (A = K, Rb, Cs) offer us more insights to study the relationship between superconductivity and topological properties, and to design innate topological superconductors.

Abstract This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predictions across the transition metals for two leading MLFF models: a kernel-based atomic cluster expansion method implemented using sparse Gaussian processes (FLARE), and an equivariant message-passing neural network (NequIP). Early transition metals present higher relative errors and are more difficult to learn relative to late platinum- and coinage-group elements, and this trend persists across model architectures. Trends in complexity of interatomic interactions for different metals are revealed via comparison of the performance of representations with different many-body order and angular resolution. Using arguments based on perturbation theory on the occupied and unoccupied d states near the Fermi level, we determine that the large, sharp d density of states both above and below the Fermi level in early transition metals leads to a more complex, harder-to-learn potential energy surface for these metals. Increasing the fictitious electronic temperature (smearing) modifies the angular sensitivity of forces and makes the early transition metal forces easier to learn. This work illustrates challenges in capturing intricate properties of metallic bonding with current leading MLFFs and provides a reference data set for transition metals, aimed at benchmarking the accuracy and improving the development of emerging machine-learned approximations.

Abstract Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed number of cycles, and are limited to a single discharge protocol. Here, an attention-based recurrent algorithm for neural analysis (ARCANA) architecture is developed and trained on an ultra-large, proprietary dataset from BASF and a large Li-ion dataset gathered from literature across the globe. ARCANA generalizes well across this diverse set of chemistries, electrolyte formulations, battery designs, and cycling protocols and thus allows for an extraction of data-driven knowledge of the degradation mechanisms. The model’s adaptability is further demonstrated through fine-tuning on Na-ion batteries. ARCANA advances the frontier of large-scale time series models in analytical chemistry beyond textual data and holds the potential to significantly accelerate discovery-oriented battery research endeavors.

Abstract Rare-earth engineering is an effective way to introduce and tune magnetism in topological kagome materials, which have been acting as a fertile platform to investigate the quantum interactions between geometry, topology, spin, and correlation. Here, we report the synthesis, structure, and physical properties of titanium-based kagome metals RETi3Bi4 (RE = Yb, Pr, and Nd) with various magnetic states. They all crystallize in the orthogonal space group Fmmm (No. 69), featuring distorted titanium kagome lattices and rare-earth zig-zag chains. By changing the rare earth atoms in the zig-zag chains, the magnetism can be tuned from nonmagnetic YbTi3Bi4 to short-range ordered PrTi3Bi4 (T anomaly ~ 8.2 K), and finally to ferromagnetic NdTi3Bi4 (T c ~ 8.5 K). In-situ resistance measurements of NdTi3Bi4 under high pressure further reveal a tunable ferromagnetic ordering temperature. These results highlight RETi3Bi4 as a promising family of kagome metals to explore nontrivial band topology and exotic phases.

Abstract There is a significant deficiency in perovskite solar module (PSM) stability under thermomechanical stressors which is not well-understood. In this perspective, common issues seen with perovskite solar cell device fabrication related to thermomechanical reliability of PSM processing are discussed, with a focus on how the robustness of device layers and interlayer adhesion can be improved. Film stresses, adhesion of charge transport layers, and instability under light and heat are discussed with the purpose of providing insight on designing PSMs for durability. Processing conditions of encapsulation of PSMs and critical parameters to consider are also examined, and accelerated testing protocols for PSMs are discussed that probe mechanical degradation modes and ensure reliability of devices in the field.

Abstract Solid-state spin qubits within silicon crystals at mK temperatures show great promise in the realisation of a fully scalable quantum computation platform. Qubit coherence times are limited in natural silicon owing to coupling to the 29Si isotope which has a non-zero nuclear spin. This work presents a method for the depletion of 29Si in localised volumes of natural silicon wafers by irradiation using a 45 keV 28Si focused ion beam with fluences above 1 × 1019 ions cm−2. Nanoscale secondary ion mass spectrometry analysis of the irradiated volumes shows residual 29Si concentration down to 2.3 ± 0.7 ppm and with residual C and O comparable to the background concentration in the unimplanted wafer. After annealing, transmission electron microscopy lattice images confirm the solid phase epitaxial re-crystallization of the as-implanted amorphous enriched volume extending over 200 nm in depth.

Abstract Health monitoring of infant patients in intensive care can be especially strenuous for both the patient and their caregiver, as testing setups involve a tangle of electrodes, probes, and catheters that keep the patient bedridden. This has typically involved expensive and imposing machines, to track physiological metrics such as heart rate, respiration rate, temperature, blood oxygen saturation, blood pressure, and ion concentrations. However, in the past couple of decades, research advancements have propelled a world of soft, wearable, and non-invasive systems to supersede current practices. This paper summarizes the latest advancements in neonatal wearable systems and the different approaches to each branch of physiological monitoring, with an emphasis on smart skin-interfaced wearables. Weaknesses and shortfalls are also addressed, with some guidelines provided to help drive the further research needed.

Abstract The tetragonal tungsten bronze, Sr2NaNb5O15, shows promise for application in high-temperature high-efficiency capacitors vital for the sustainable energy revolution. Previously, the structural complexity of this and related materials has obscured the mechanisms underpinning two large anomalies in relative permittivity (ε r) which give rise to their exceptionally broad dielectric response. Here, we comprehensively investigate the structural evolution from −173 to 627 °C, combining electron, X-ray and neutron diffraction, electron microscopy, and first principles electronic structure calculations to unambiguously identify the structural origins of both anomalies. The peak in ε r at 305 °C is associated with a polar-nonpolar phase transition, wherein cations displace along the c axis. Guided by DFT, we identify a further transition upon cooling, associated with the second peak at −14 °C, linked to the softening of an in-plane polar distortion with a correlation length limited by ferroelastic nano-domains arising from rigid-unit-like tilting of NbO6 octahedra at high temperature, imparting relaxor-like behaviour. Thus, the two dielectric anomalies in Sr2NaNb5O15 are associated with two distinct crystallographic phase transitions and their interplay with a microstructure that arises from a third, non-polar structural distortion. Chemical control of these will enable development of tuneable materials with dielectric properties suitable for high-temperature energy storage applications.

Abstract Developing the field of neuromorphic quantum computing necessitates designing scalable quantum memory devices. Here, we propose a superconducting quantum memory device in the microwave regime, termed a microwave quantum memcapacitor. It comprises two linked resonators, the primary one is coupled to a Superconducting Quantum Interference Device, which allows for the modulation of the resonator properties through external magnetic flux. The auxiliary resonator, operated through weak measurements, provides feedback to the primary resonator, ensuring stable memory behavior. This device operates with a classical input in one cavity while reading the response in the other, serving as a fundamental building block toward arrays of microwave quantum memcapacitors. We observe that a bipartite setup can retain its memory behavior and gains entanglement and quantum correlations. Our findings pave the way for the experimental implementation of memcapacitive superconducting quantum devices and memory device arrays for neuromorphic quantum computing.

Abstract Marine structures such as ports, bridges, pipelines, vessels, and platforms are an essential part of modern infrastructure, where the use of higher-strength steel provides savings in logistics and construction. However, the repair of higher-strength steels can be challenging, especially underwater. Wet shielded metal arc welding is the most widely used and least expensive method for underwater welding repairs, but is very susceptible to hydrogen-induced cracking. Thus, researchers and welding engineers aim to reduce the amount of hydrogen in the weld material. Recent success has been achieved through the use of austenitic welding consumables, such as austenitic stainless steel and nickel-based electrodes. The use of these consumables drastically reduces the amount of diffusible hydrogen in the weld metal. However, these austenitic materials usually have different corrosion potential as compared to the structural steel the weld beads are applied to. This creates the risk of severe galvanic corrosion. In the presented study, the corrosion behavior of welds created with austenitic stainless steel and nickel-based electrodes were studied. Samples were aged for 1.5 years in the Baltic Sea. Simultaneously, the effectiveness of corrosion protection systems such as coating and Impressed Current Cathodic Protection (ICCP) were evaluated. Localized corrosion occurred in the heat-affected zone when austenitic electrodes were used in the corrosive environment. The localized corrosion depth after 1.5 years in the Baltic Sea and in the salt spray layer was approximately 250 µm and 390 µm, respectively. The ICCP system and the use of a coating were effective in preventing localized corrosion. The low pitting corrosion density of 2.5 × 103 m−2 corresponds to grade A1 according to the standard and was found to be negligible as compared to the localized corrosion in the heat-affect zone.

Abstract In this work, the passivation and localized corrosion of selective laser melted (SLM) stainless steel 316 L when exposed to high pressures of CO2 with the presence of H2S and Cl− at 25 °C and 125 °C were studied. Depletion of Cr/Mo was observed at the cell interiors and melt-pool boundaries (MPBs) compared to the cell boundaries. Volta potential differences obtained from scanning Kelvin probe force microscopy (SKPFM) showed that the MPBs were 8–20 mV lower than the matrix, while the cell interiors were 20–50 mV lower than the cell boundaries. Electrochemical impedance spectroscopy (EIS) and Mott–Schottky tests indicated a more defective passive film at 125 °C, and X-ray photoelectron spectroscopy (XPS) confirmed the formation of a less protective film with an increased S/O ratio at 125 °C than 25 °C. Initiation of localized corrosion was observed at the MPBs and pits formed after a week of immersion were wider by an order of magnitude at 125 °C than 25 °C, with evidence of cell-interior dissolution. While passivity was observed even at elevated temperatures, local chemical heterogeneities compromised the stability of the film and contributed to localized corrosion in SLM SS316L.

Abstract Due to its excellent biocompatibility, pure iron is a very promising implant material, but often features corrosion rates that are too low. Using additive manufacturing and modified powders the microstructure and, thus, the material properties, e.g., the corrosion properties, can be tailored for specific applications. Within the scope of this study, pure iron powder was modified with different amounts of CeO2 or Fe2O3 nanoparticles and subsequently processed by Electron Beam Powder Bed Fusion (PBF-EB/M). The corrosion-fatigue behavior of CeO2 and Fe2O3 modified iron was investigated using rotation bending tests under the influence of simulated body fluid (m-SBF). While the modification using Fe2O3 showed reduced fatigue and corrosion-fatigue strengths, it could be demonstrated that the modification with CeO2 is characterized by improved fatigue properties. The superior fatigue properties in air are attributed to the positive impact of dispersion strengthening. Additionally, an increased degradation rate compared to pure iron could be observed, eventually promoting an earlier failure of the specimens in the corrosion fatigue tests.

Abstract The escalating global challenge of antimicrobial resistance demands innovative approaches. This review delves into the current status and future prospects of bioengineered metallic nanoparticles derived from natural sources as potent antimicrobial agents. The unique attributes of metallic nanoparticles and the abundance of natural resources have sparked a burgeoning field of research in combating microbial infections. A systematic review of the literature was conducted, encompassing a wide range of studies investigating the synthesis, characterization, and antimicrobial mechanisms of bioengineered metallic nanoparticles. Databases such as PubMed, Scopus, Web of Science, ScienceDirect, Springer, Taylor & Francis online and OpenAthen were extensively searched to compile a comprehensive overview of the topic. The synthesis methods, including green and sustainable approaches, were examined, as were the diverse biological sources used in nanoparticle fabrication. The amalgamation of metallic nanoparticles and natural products has yielded promising antimicrobial agents. Their multifaceted mechanisms, including membrane disruption, oxidative stress induction, and enzyme inhibition, render them effective against various pathogens, including drug-resistant strains. Moreover, the potential for targeted drug delivery systems using these nanoparticles has opened new avenues for personalized medicine. Bioengineered metallic nanoparticles derived from natural sources represent a dynamic frontier in the battle against microbial infections. The current status of research underscores their remarkable antimicrobial efficacy and multifaceted mechanisms of action. Future prospects are bright, with opportunities for scalability and cost-effectiveness through sustainable synthesis methods. However, addressing toxicity, regulatory hurdles, and environmental considerations remains crucial. In conclusion, this review highlights the evolving landscape of bioengineered metallic nanoparticles, offering valuable insights into their current status and their potential to revolutionize antimicrobial therapy in the future. Graphical Abstract

Abstract Biomedical nanocomposites, which are an upcoming breed of mischievous materials, have ushered in a new dimension in the healthcare sector. Incorporating these materials tends to boost features this component already possesses and give might to things these components could not withstand alone. The biopolymer, which carries the nanoparticles, can simultaneously improve the composite's stiffness and biological characteristics, and vice versa. This increases the options of the composite and the number of times it can be used. The bio-nanocomposites and nanoparticles enable the ecocompatibility of the medicine in their biodegradability, and they, in this way, have ecological sustainability. The outcome is the improved properties of medicine and its associated positive impact on the environment. They have broad applications in antimicrobial agents, drug carriers, tissue regeneration, wound care, dentistry, bioimaging, and bone filler, among others. The dissertation on the elements of bio-nanocomposites emphasizes production techniques, their diverse applications in medicine, match-up issues, and future-boasting prospects in the bio-nanocomposites field. Through the utilization of such materials, scientists can develop more suitable for the environment and healthy biomedical solutions, and world healthcare in this way improves as well.

Abstract Curcumin is a polyphenol extracted from Curcuma longa’s roots. Low doses of curcumin are related to anti-inflammatory, antioxidant, and neuroprotective effects, while high doses are used for their lethality. This diversity of behaviors allows us to understand curcumin as a compound with hormetic action. Due to its strongly hydrophobic character, curcumin is often solubilized in organic compounds. In this way, we have recently reported the undesirable and occasionally irreversible effects of alcohol and DMSO on the viability of primary Schwann cell cultures. In this scenario, the use of nanoparticles as delivery systems has become a successful alternative strategy for these compounds. In the present work, we describe the structure of Polydopamine (PDA) nanoparticles, loaded with a low dose of curcumin (Curc-PDA) without the use of additional organic solvents. We analyzed the curcumin released, and we found two different forms of curcumin. Small increased cell viability and proliferation were observed in endoneurial fibroblast and Schwann cell primary cultures when Curc-PDA was steadily supplied for 5 days. The increased bioavailability of this natural compound and the impact on cells in culture not only confirm the properties of curcumin at very low doses but also provide a glimpse of a possible therapeutic alternative for PNS conditions in which SCs are involved.

Abstract We present the first work of the synthesis mechanism from graphene quantum dots (GQDs) to carbon nanotubes (CNTs) by an ion-sputtering assisted chemical vapor deposition. During the annealing process, a Pt thin film deposited by the ion-sputtering was dewetted and agglomerated to form many nanometer-sized particles, leading to Pt nanoparticles (PtNPs) that can act as catalysts for creating carbon allotropes. The shape of the allotropes can be effectively tailored from GQDs to CNTs by controlling three key parameters such as the dose of catalytic ions (D), amounts of carbon source (S), and thermal energy (T). In our work, it was clearly proved that the growth control from GQDs to CNTs has a comparably proportional relationship with D and S, but has a reverse proportional relationship with T. Furthermore, high-purity GQDs without any other by-products and the CNTs with the cap of PtNPs were generated. Their shapes were appropriately controlled, respectively, based on the established synthesis mechanism. Graphical abstract

Abstract Temporal resolution is a key parameter in the observation of dynamic processes, as in the case of single molecules motions visualized in real time in two-dimensions by wide field (fluorescence) microscopy, but a systematic investigation of its effects in all the single particle tracking analysis steps is still lacking. Here we present tools to quantify its impact on the estimation of diffusivity and of its distribution using one of the most popular tracking software for biological applications on simulated data and movies. We found important shifts and different widths for diffusivity distributions, depending on the interplay of temporal sampling conditions with various parameters, such as simulated diffusivity, density of spots, signal-to-noise ratio, lengths of trajectories, and kind of boundaries in the simulation. We examined conditions starting from the ones of experiments on the fluorescently labelled receptor p75NTR, a relatively fast-diffusing membrane receptor (diffusivity around 0.5–1 µm2/s), visualized by TIRF microscopy on the basal membrane of living cells. From the analysis of the simulations, we identified the best conditions in cases similar to these ones; considering also the experiments, we could confirm a range of values of temporal resolution suitable for obtaining reliable diffusivity results. The procedure we present can be exploited in different single particle/molecule tracking applications to find an optimal temporal resolution.

Abstract Using water as a renewable and safe energy source for hydrogen generation has reduced the need to use toxic fossil fuels. Photocatalytic approaches provide a worthy solution to avoid the high expenditure on complicated electrochemical pathways to promote Hydrogen Evolution Reactions. However, several types of photocatalysts including noble metal-based catalysts have already been in use for this purpose, which are generally considered high-cost as well. The present study aims to use the benefits of metal–organic frameworks (MOFs) with semiconductor-like characteristics, highly porous structures and high design flexibility. These properties of MOFs allow more efficient and effective mass transport as well as exposure to light.in this paper, using MOF technology and benefiting from the characteristics of Fe3O4 nanoparticles as catalyst support for more efficient separation of catalyst, we have synthesized a novel composite. Our proposed photocatalyst demonstrates efficient harvest of light in all wavelengths from UV to visible to generate electron/hole pairs suitable for water splitting with a turnover frequency of 0.222 h−1 at ambient conditions without requiring any additives. Graphical abstract