Current Opinion in Solid State & Materials Science最新文献

The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials science methods, and ML methods) used within papers that apply ML to materials science. The analysis demonstrates that despite the growth of deep learning techniques, the use of classical machine learning is still dominant as a whole. It also demonstrates how new research can effectively build upon past research, particular in the domain of ML models trained on density functional theory calculation data. Next, I present the progression of best scores as a function of time on the matbench materials science benchmark for formation enthalpy prediction. In particular, a dramatic improvement of 7 times reduction in error is obtained when progressing from feature-based methods that use conventional ML (random forest, support vector regression, etc.) to the use of graph neural network techniques. Finally, I provide views on future challenges and opportunities, focusing on data size and complexity, extrapolation, interpretation, access, and relevance.

The ever-expanding capabilities of machine learning are powered by exponentially growing complexity of deep neural network (DNN) models, requiring more energy and chip-area efficient hardware to carry out increasingly computational expensive model-inference and training tasks. Electrochemical random-access memories (ECRAMs) are developed specifically to implement efficient analog in-memory computing for these data-intensive workloads, showing some critical advantages over competing memory technologies mostly developed originally for digital electronics. ECRAMs possess the distinctive capability to switch between a very large number of memristive states with a high level of symmetry, small cycle-to-cycle variability, and low energy consumption; and they simultaneously exhibit good endurance, long data retention, fast switching speed up to nanoseconds, and verified scalability down to sub-50 nm regime, therefore holding great promise in realizing deep-learning accelerators when heterogeneously integrated with silicon-based peripheral circuits. In this review, we first examine challenges in constructing in-memory-computing accelerators and unique advantages of ECRAMs. We then critically assess the various ionic species, channel materials, and solid-state electrolytes employed in ECRAMs that influence device programming characteristics and performance metrics with their different memristive modulation and ionic transport mechanisms. Furthermore, ECRAM device engineering and integration schemes are discussed, within the context of their implementation in high-density pseudo-crossbar array microarchitectures for performing DNN inference and training with high parallelism. Finally, we offer our insights regarding major remaining obstacles and emerging opportunities of harnessing ECRAMs to realize deep-learning accelerators through material-device-circuit-architecture-algorithm co-design.

The application of electric current on metallic materials alters the microstructures and mechanical properties of materials. The improved formability and accelerated microstructural evolution in material via the application of electric current is referred to as electric current-induced phenomena. This review includes extensive experimental and computational studies on the deformation behavior and microstructural evolutions of metallic materials, underlying mechanisms, and practical applications in industry. We precisely introduce various electric current-induced effects by considering different materials and electric conditions. The discussion covers the mechanisms underlying these effects, emphasizing both thermal and athermal effects of electric current, supported by experimental evidence, physical principles, atomic-scale simulations, and numerical methods. Furthermore, we explore the applications of electric current-induced phenomena in material processing techniques including electrically-assisted forming, treatment, joining, and machining. This review aims to deepen the understanding of how electric currents affect metallic materials and inspire further development of advanced fabrication and processing technologies in time- and energy-efficient ways.

New materials are a fundamental component of most major advancements in human history. The pivotal role materials play in the development of next generation technologies has spurred campaigns such as the Materials Genome Initiative (MGI) with the goal of reducing the time and cost to discover, characterize, and deploy advanced materials. As goals of the MGI have been met and new capabilities have emerged, a contemporary vision has taken shape within the scientific community whereby the exploration of materials space is dramatically accelerated by artificial intelligence agent(s) capable of performing research independently from humans and achieving a paradigm change in the field. As this idea comes to fruition and new materials are more rapidly computationally evaluated and synthesized nearly on demand, the rate at which a complete characterization of each candidate material’s properties can be completed and understood within the context of all other potential solutions will be the next bottleneck in a materials design campaign. This work provides an overview of the technical and conceptual components related to materials characterization discussed during a workshop dedicated to challenging the way materials research is thought of and performed within the emergent field of autonomous materials research and design (AMRAD). Furthermore, general considerations for developing autonomous characterization are presented along with related works and a discussion of their progress and shortcomings toward the AMRAD vision.

The understanding of when a thin film is two-dimensional (2D) varies throughout the literature. It was introduced by advances in nanotechnology that allowed the fabrication of structures that are in the nm scale in one dimension. More recently, materials with atomic thickness, such as graphene and other van der Waals materials, allowed us to isolate structures that have reached the ultimate limit of thickness. Their layered structures allow a straightforward identification of the monolayers as 2D structures. Today, 2D structures are reported from a wide class of materials ranging from molecules to thin non-van der Waals layers, generating interest across a large variety of scientific fields. The thickness of these reported 2D films varies from atomic scale to several tens or even hundreds of nm. This puzzling occurrence of several hundred nm thick ‘2D materials’ calls for a critical assessment of when thin films are present as 2D. Here, we explore aspects such as atomic and electronic structure, chemical bonding, composition, and the relation of bulk-to-thin film characteristics to find criteria that describe 2D structures. With that, we aim to fuel an interdisciplinary dialogue towards establishing clear definitions for when a thin film is a 2D structure.

Ferroelectric and two-dimensional (2D) materials are both heavily investigated classes of electronic materials. This is unsurprising since they both have superlative fundamental properties and high-value applications in computing, sensing etc. In this Perspective, we investigate the research topics where 2D semiconductors and ferroelectric materials both in 2D or 3D form come together. 2D semiconductors have unique attributes due to their van der Waals nature that permits their facile integration with any other electronic or optical materials. In addition, the emergence of ferroelectricity in 2D monolayers, multilayers, and artificial structures offers further advantages since traditionally ferroelectricity has been difficult to achieve in highly thickness scaled materials. Further, we elaborate on the applications of 2D materials + ferroelectricity in non-volatile memory devices, highlighting their potential for in-memory computing, neuromorphic computing, optoelectronics, and spintronics. We also suggest the challenges posed by both ferroelectrics and 2D materials, including material/device preparation and reliable characterizations, to drive further investigations at the interface of these important classes of electronic materials.

High-entropy alloys (HEAs) have become an important topic in modern materials science due to their exceptional properties. Despite their attractive properties, achieving a superior strength-ductility synergy has been, and remains, a major challenge. In practice, overcoming the strength-ductility trade-off in HEAs is an overriding priority which may open the opportunity for the development of high-performance alloys. It is well-established that high-strength steels benefitted from metastability engineering by manipulating the deformation mechanisms to facilitate a deformation-induced martensitic transformation which provides acceptable ductility. Accordingly, and following this same approach, a metastable HEA was developed which exhibited a desirable combination of strength and ductility. This review is designed specifically to give a comprehensive description of the deformation mechanisms in these materials and to provide an overall perspective on the importance of material characteristics and processing variables. The discussion is centred for different HEAs on the significance of the transformation-induced plasticity in breaking the strength-ductility trade-off and thereafter to examine some challenges and research gaps which require future attention. The understanding of the HEAs achieved to date demonstrates that there is a large potential for the future enhancement and optimization of these alloys in developing high-performance materials for a wide range of applications.

High-quality, large-scale graphene holds significant potential for future electronic applications because of its exceptional properties. Among the various graphene production methods, chemical vapor deposition (CVD) has emerged as a promising approach for the industrial-scale fabrication of electronic-grade graphene films. Although large-area graphene films are being produced using advanced variants of conventional CVD systems, their quality can be further improved. In the past decade, significant progress has been made in the CVD-based fabrication of large-area, high-quality graphene, driven by strategies for controlling growth parameters such as the heating mode in CVD, graphene nucleation density, and crystal orientation of the growth substrate. In this review, we present key findings on the CVD-based production of large-area, high-quality graphene using established strategies, and highlight the advantages and challenges. Additionally, we introduce a novel approach to growing high-quality graphene based on recrystallization—the use of a mobile hot-wire CVD system that can provide localized heat energy in a dynamic manner. We cover various synthesis strategies that leverage this system to induce changes in graphene properties and explore their potential applications. Finally, based on a comprehensive understanding of the corresponding growth mechanisms, we offer insights into the CVD-based synthesis of large-area, high-quality graphene films and examine its prospects.

Wearable strain sensors are emerging as promising devices for monitoring human motions and physiological signals in various fields, such as healthcare, robotics, and sports. Among various materials, polymer–graphene nanocomposites (PGNs) have attracted considerable attention due to their excellent mechanical, electrical, and thermal properties, as well as their facile fabrication methods. This review summarised the recent progress and challenges of PGN-based wearable strain sensors for physiological signal monitoring. First, the classification of PGNs based on the structural derivatives of graphene (such as graphene sheets, graphene oxide, reduced graphene oxide, and graphene quantum dots) and the strain sensing mechanisms (such as resistive and capacitive) were introduced. Then, we discussed the fabrication approaches of PGN-based strain sensors, including solution processing, melt blending, in-situ polymerization, spinning, printing, and coating. Afterward, this article highlighted the functional PGN-based strain sensors using various polymers and their applications in monitoring subtle and significant physiological signals. Finally, this work identified the underlying challenges and future perspectives of PGN-based wearable strain sensors for accurate and reliable physiological signal monitoring. This review provides a comprehensive overview of the current state-of-the-art of PGN-based wearable strain sensors and inspires further research in this field.