Current Opinion in Solid State & Materials Science最新文献

Over the past 2 decades, the computational materials science community has made great advances in facilitating and supporting the development of new materials, particularly metallic alloys. While the materials community now has impactful computational tools, from Calculation of Phase Diagrams (CALPHAD) methods for computing phase diagrams, to density functional theory (DFT) for computing certain properties of individual phases, to Artificial Intelligence (AI) and Machine Learning (ML) to accelerate computational discoveries, experimental validation methods, in any high-throughput methodology, has been lacking. Metallic alloy synthesis has remained incredibly slow owing to traditional methods, such as arc-melting methods, remaining a one-off approach, which each individual sample requiring a separate sample preparation and characterization process, little if any of which is automated. To overcome these limitations, the High-Throughput Rapid Experimental Alloy Development (HT-READ) platform was developed. The HT-READ platform is a true paradigm change in the field of metallic alloy development, enabling fully automated synthesis and characterization of alloy samples in groups of 16 samples at once. The enabling feature of the HT-READ platform approach is the use of a single sample, with up to 16 individual alloy ‘spokes’ comprising a ‘wagon-wheel’ geometry. This geometry directly enables the automation of each of the characterization steps that can proceed without instrument operation by a trained engineer. In spite of the significant advantages of the HT-READ platform, the rate controlling step remains the physical weighing of the alloy powders used in the 3-D printing of the individual spokes of the ‘wagon-wheel’ sample. In the newly updated HT-READ platform, the powder handling and weighting process has now been automated using a ChemSpeed™ Doser, which can dispense up to 24 different powders, which might be needed to achieve the desired composition for each of the 16-spoke samples. With the Updated HT-READ platform, it is now possible to achieve truly high-throughput of metallic alloy development, with automated characterization across multiple instruments, from GDS, XRD, SEM-EDS, SEM-EBSD, microhardness, and nanoindentation.

Composed of a large number of artificial nanostructures, metasurfaces have found applications in metalenses, structured light generation and optical deflectors through wavefront shaping. After careful design according to optical requirements, metasurfaces can achieve independent functions under different incident light conditions. Deep learning emerges as a transformative design approach in nanophotonics, providing nanostructures tailored to various optical requirements. A statistic relationship between geometric shapes and optical properties is hidden in massive nanostructures. The relationship is learned without any help of physical models, opening a possibility for further research on multifunctional metasurface. Here, different optical dimensions multiplexed in metasurfaces are reviewed, and combining these multiplexing methods into one metasurface can significantly increase functional channels. Then different types of neural networks applied in metasurface design are introduced, opening a possibility to combine the various optical multiplexing. Furthermore, the constructive suggestions are provided on multifunctional metasurface designed by deep learning, and specific opinions on future developments are discussed.

Metamaterials are specially engineered materials distinguished by their unique properties not typically seen in naturally occurring materials. However, the challenge lies in achieving lightweight yet mechanically rigid architectures, as these properties are sometimes conflicting. For example, buckling strength is a critical property that needs to be enhanced since buckling can cause catastrophic failure of the lightweight metamaterials. In this study, we introduce a generative machine learning based approach to determine the superior geometries of metamaterials to maximize their buckling strength without compromising their elastic modulus. Our results, driven by machine learning based design, remarkably enhanced buckling strength (over 90 %) compared to conventional metamaterial designs. The simulation results are validated by a series of experimental testing and the mechanism of the high buckling strength is elucidated by correlating stress field with the metamaterial geometry. Our results provide insights into the interplay between shape and buckling strength, unveiling promising avenues for designing efficient metamaterials in future applications.

Conformal lattice materials (cell sizes ranging from nanometres to millimetres), including conformal metal lattice metamaterials, are cellular materials or structures that conform to all or part of the physical space of a product with topologically complete boundary cells. Enabled by powder bed fusion (PBF) additive manufacturing (AM), conformal metal lattice metamaterials provide an innovative solution for lightweight engineering or integration of structure and function. A key step in their fabrication is to generate a conformal lattice model suitable for PBF AM. This research reviews their design methods and evaluates each method using seven criteria. These include (i) the sequence of geometric modelling and lattice topology generation (sequential or simultaneous), (ii) integrity of lattice cell topology at boundaries, (iii) compatibility with lattice cell types, (iv) applicability to design geometry, (v) ease of coding, (vi) accessibility via common software tools, and (vii) ability to define strut inclination angles in a complex conformal design space. On this basis, various laser PBF (LPBF) manufacturability issues of conformal metal lattices are considered, and two Ti-6Al-4V conformal lattices are fabricated using LPBF and evaluated. This review provides a necessary foundation for future research and applications of conformal lattice metamaterials in various engineering fields.

In this modern era, the necessity of a safe environment with a swift detection of even minute concentrations of hazardous and combustible gases has spurred significance in the advancement of gas sensor technology. In this aspect, the room temperature operable gas sensors have marked their importance by ensuring the safe detection of combustible gases. Nonetheless, the incomplete recovery of such gas sensors requires thermal activation, which entails several limitations. Therefore, the light-activation of gas sensors has garnered considerable attention owing to its compactness and cost-effective operations. The light-activation generates the electron-hole pairs which activate the sensing surface and modulate the charge carrier concentration, thereby enhancing the gas-sensing performances. In this review, the gas-sensing performances of various photoactive sensing materials including metal oxides and two-dimensional materials under light irradiation have been discussed. The gas sensors based on metal oxide and two-dimensional materials have shown significant performance in terms of response, as well as sharp response and recovery times under both ultra-violet and visible light illumination. Finally, this review emphasizes the challenges and future scopes associated with the light-activated room temperature operable gas sensors, which could lead a pathway toward the development of an ultrafast gas sensor.

Ligands are the key to almost any strategy in the assembly of programmable nanocrystals (or nanoparticles) and must be accurately considered in any predictive model. Hard Spheres (or Shapes) provide the simplest and yet quite successful approach to assembly, with remarkable sophisticated predictions verified in experiments. There are, however, many situations where hard spheres/shapes predictions fail. This prompts three important questions: In what situations should hard spheres/shapes models be expected to work? and when they do not work, Is there a general model that successfully corrects hard sphere/shape predictions? and given other successful models where ligands are included explicitly, and of course, numerical simulations, can we unify hard sphere/shape models, explicit ligand models and all atom simulations?. The Orbifold Topological Model (OTM) provides a positive answer to these three questions. In this paper, I give a detailed review of OTM, describing the concept of ligand vortices and how it leads to spontaneous valence and nanoparticle “eigenshapes” while providing a prediction of the lattice structure, without fitting parameters, which accounts for many body effects not captured by (two-body) potentials. I present a thorough survey of experiments and simulations and show that, to this date, they are in full agreement with the OTM predictions. I conclude with a discussion on whether NC superlattices are equilibrium structures and some significant challenges in structure prediction.

Ultra-wide bandgap semiconductors, with bandgaps greater than 3.5 eV, have immense potential in power-switching electronic applications and ultraviolet light emitters. But the development of these materials faces a number of challenges, many of which relate to controlling electrical conductivity. In this work, we review the major obstacles for a set of these materials (focusing on AlGaN, AlN, BN, Ga₂O₃, Al₂O₃, and diamond) including limitations in n- and p-type doping and the effects of impurities and native point defects. We present an in-depth discussion on ultra-wide-bandgap nitride and oxide semiconductors, which face several similar challenges, as well as diamond, which presents a more unique scenario. The biggest obstacle for these semiconductors is attaining bipolar electrical conductivity, which means achieving both n-type and p-type conductivity within the same material. Toward this end, we also discuss potential future research directions that may lead to the development of bipolar ultra-wide bandgap semiconductor devices.

If the promise of generative modeling techniques is realized, it may fundamentally change how we carry out molecular simulation. The suite of techniques and models collectively termed “generative AI” includes many different classes of models built for varied types of data, from natural language to images. Recent advances in the machine learning literature that construct ever better generative models, though, do not contend with the challenges unique to complex, molecular systems. To generate a statistically likely molecular configuration, many correlated degrees of freedom must be sampled together, while also satisfying the strong constraints of chemical physics. Recent efforts to develop generative models for biomolecular systems have shown spectacular results in some cases—nevertheless, some simple systems remain out of reach with our present methodology. Arguably, the central concern is data efficiency: we should aim to train models that can meaningfully generalize beyond their training data and hence facilitate discovery. In this review, we discuss methods and future directions for directly incorporating physics-based models into generative neural networks, which we believe is a crucial step for addressing the limitations of the current toolkit.

For over a decade, machine learning (ML) models have been making strides in computer vision and natural language processing (NLP), demonstrating high proficiency in specialized tasks. The emergence of large-scale language and generative image models, such as ChatGPT and Stable Diffusion, has significantly broadened the accessibility and application scope of these technologies. Traditional predictive models are typically constrained to mapping input data to numerical values or predefined categories, limiting their usefulness beyond their designated tasks. In contrast, contemporary models employ representation learning and generative modeling, enabling them to extract and encode key insights from a wide variety of data sources and decode them to create novel responses for desired goals. They can interpret queries phrased in natural language to deduce the intended output. In parallel, the application of ML techniques in materials science has advanced considerably, particularly in areas like inverse design, material prediction, and atomic modeling. Despite these advancements, the current models are overly specialized, hindering their potential to supplant established industrial processes. Materials science, therefore, necessitates the creation of a comprehensive, versatile model capable of interpreting human-readable inputs, intuiting a wide range of possible search directions, and delivering precise solutions. To realize such a model, the field must adopt cutting-edge representation, generative, and foundation model techniques tailored to materials science. A pivotal component in this endeavor is the establishment of an extensive, centralized dataset encompassing a broad spectrum of research topics. This dataset could be assembled by crowdsourcing global research contributions and developing models to extract data from existing literature and represent them in a homogenous format. A massive dataset can be used to train a central model that learns the underlying physics of the target areas, which can then be connected to a variety of specialized downstream tasks. Ultimately, the envisioned model would empower users to intuitively pose queries for a wide array of desired outcomes. It would facilitate the search for existing data that closely matches the sought-after solutions and leverage its understanding of physics and material-behavior relationships to innovate new solutions when pre-existing ones fall short.

Fluorescence lifetime imaging microscopy (FLIM) is a powerful imaging tool offering molecular specific insights into samples through the measurement of fluorescence decay time, with promising applications in diverse research fields. However, to acquire two-dimensional lifetime images, conventional FLIM relies on extensive scanning in both the spatial and temporal domain, resulting in much slower acquisition rates compared to intensity-based approaches. This problem is further magnified in three-dimensional imaging, as it necessitates additional scanning along the depth axis. Recent advancements have aimed to enhance the speed and three-dimensional imaging capabilities of FLIM. This review explores the progress made in addressing these challenges and discusses potential directions for future developments in FLIM instrumentation.