Hasil untuk "Materials Science"

K. Novoselov, A. Mishchenko, A. Carvalho et al.

BACKGROUND Materials by design is an appealing idea that is very hard to realize in practice. Combining the best of different ingredients in one ultimate material is a task for which we currently have no general solution. However, we do have some successful examples to draw upon: Composite materials and III-V heterostructures have revolutionized many aspects of our lives. Still, we need a general strategy to solve the problem of mixing and matching crystals with different properties, creating combinations with predetermined attributes and functionalities. ADVANCES Two-dimensional (2D) materials offer a platform that allows creation of heterostructures with a variety of properties. One-atom-thick crystals now comprise a large family of these materials, collectively covering a very broad range of properties. The first material to be included was graphene, a zero-overlap semimetal. The family of 2D crystals has grown to includes metals (e.g., NbSe2), semiconductors (e.g., MoS2), and insulators [e.g., hexagonal boron nitride (hBN)]. Many of these materials are stable at ambient conditions, and we have come up with strategies for handling those that are not. Surprisingly, the properties of such 2D materials are often very different from those of their 3D counterparts. Furthermore, even the study of familiar phenomena (like superconductivity or ferromagnetism) in the 2D case, where there is no long-range order, raises many thought-provoking questions. A plethora of opportunities appear when we start to combine several 2D crystals in one vertical stack. Held together by van der Waals forces (the same forces that hold layered materials together), such heterostructures allow a far greater number of combinations than any traditional growth method. As the family of 2D crystals is expanding day by day, so too is the complexity of the heterostructures that could be created with atomic precision. When stacking different crystals together, the synergetic effects become very important. In the first-order approximation, charge redistribution might occur between the neighboring (and even more distant) crystals in the stack. Neighboring crystals can also induce structural changes in each other. Furthermore, such changes can be controlled by adjusting the relative orientation between the individual elements. Such heterostructures have already led to the observation of numerous exciting physical phenomena. Thus, spectrum reconstruction in graphene interacting with hBN allowed several groups to study the Hofstadter butterfly effect and topological currents in such a system. The possibility of positioning crystals in very close (but controlled) proximity to one another allows for the study of tunneling and drag effects. The use of semiconducting monolayers leads to the creation of optically active heterostructures. The extended range of functionalities of such heterostructures yields a range of possible applications. Now the highest-mobility graphene transistors are achieved by encapsulating graphene with hBN. Photovoltaic and light-emitting devices have been demonstrated by combining optically active semiconducting layers and graphene as transparent electrodes. OUTLOOK Currently, most 2D heterostructures are composed by direct stacking of individual monolayer flakes of different materials. Although this method allows ultimate flexibility, it is slow and cumbersome. Thus, techniques involving transfer of large-area crystals grown by chemical vapor deposition (CVD), direct growth of heterostructures by CVD or physical epitaxy, or one-step growth in solution are being developed. Currently, we are at the same level as we were with graphene 10 years ago: plenty of interesting science and unclear prospects for mass production. Given the fast progress of graphene technology over the past few years, we can expect similar advances in the production of the heterostructures, making the science and applications more achievable. Production of van der Waals heterostructures. Owing to a large number of 2D crystals available today, many functional van der Waals heterostructures can be created. What started with mechanically assembled stacks (top) has now evolved to large-scale growth by CVD or physical epitaxy (bottom). The physics of two-dimensional (2D) materials and heterostructures based on such crystals has been developing extremely fast. With these new materials, truly 2D physics has begun to appear (for instance, the absence of long-range order, 2D excitons, commensurate-incommensurate transition, etc.). Novel heterostructure devices—such as tunneling transistors, resonant tunneling diodes, and light-emitting diodes—are also starting to emerge. Composed from individual 2D crystals, such devices use the properties of those materials to create functionalities that are not accessible in other heterostructures. Here we review the properties of novel 2D crystals and examine how their properties are used in new heterostructure devices.

M. Malinauskas, A. Žukauskas, S. Hasegawa et al.

Processing of materials by ultrashort laser pulses has evolved significantly over the last decade and is starting to reveal its scientific, technological and industrial potential. In ultrafast laser manufacturing, optical energy of tightly focused femtosecond or picosecond laser pulses can be delivered to precisely defined positions in the bulk of materials via two-/multi-photon excitation on a timescale much faster than thermal energy exchange between photoexcited electrons and lattice ions. Control of photo-ionization and thermal processes with the highest precision, inducing local photomodification in sub-100-nm-sized regions has been achieved. State-of-the-art ultrashort laser processing techniques exploit high 0.1–1 μm spatial resolution and almost unrestricted three-dimensional structuring capability. Adjustable pulse duration, spatiotemporal chirp, phase front tilt and polarization allow control of photomodification via uniquely wide parameter space. Mature opto-electrical/mechanical technologies have enabled laser processing speeds approaching meters-per-second, leading to a fast lab-to-fab transfer. The key aspects and latest achievements are reviewed with an emphasis on the fundamental relation between spatial resolution and total fabrication throughput. Emerging biomedical applications implementing micrometer feature precision over centimeter-scale scaffolds and photonic wire bonding in telecommunications are highlighted. The ability of femtosecond lasers to efficiently fabricate complex structures and devices for a wide variety of applications is reviewed. Mangirdas Malinauskas at Vilnius University in Lithuania and co-workers in Japan, Australia and Saudi Arabia describe how state-of-the-art laser processing techniques with ultrashort light pulses can be used to structure materials with a sub-micrometre resolution. Direct laser writing of suitable photoresists and other transparent media can create intricate three-dimensional photonic crystals, micro-optical components, gratings, tissue scaffolds and optical waveguides. Such structures are potentially useful for empowering next-generation applications in telecommunications and bioengineering that rely on the creation of increasingly sophisticated miniature parts. The precision, fabrication speed and versatility of ultrafast laser processing make it well placed to become a vital industrial tool for manufacturing.

I. Steinbach

B. Carter, M. G. Norton, L. Wang

Ilyes Batatia, Philipp Benner, Chiang Yuan et al.

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model-and its qualitative and at times quantitative accuracy-on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.

W. Binder, R. Sachsenhofer

Jean‐François Lutz

P. Stadelmann

B. Ferguson, Xicheng Zhang

E. Kaldis

Xiaolong Liu, M. Hersam

L. Ghiringhelli, J. Vybíral, S. Levchenko et al.

Statistical learning of materials properties or functions so far starts with a largely silent, nonchallenged step: the choice of the set of descriptive parameters (termed descriptor). However, when the scientific connection between the descriptor and the actuating mechanisms is unclear, the causality of the learned descriptor-property relation is uncertain. Thus, a trustful prediction of new promising materials, identification of anomalies, and scientific advancement are doubtful. We analyze this issue and define requirements for a suitable descriptor. For a classic example, the energy difference of zinc blende or wurtzite and rocksalt semiconductors, we demonstrate how a meaningful descriptor can be found systematically.

H. Bunge

K. Jablonka, D. Ongari, S. M. Moosavi et al.

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal–organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.

Leopold Talirz, Snehal Kumbhar, Elsa Passaro et al.

Materials Cloud is a platform designed to enable open and seamless sharing of resources for computational science, driven by applications in materials modelling. It hosts (1) archival and dissemination services for raw and curated data, together with their provenance graph, (2) modelling services and virtual machines, (3) tools for data analytics, and pre-/post-processing, and (4) educational materials. Data is citable and archived persistently, providing a comprehensive embodiment of entire simulation pipelines (calculations performed, codes used, data generated) in the form of graphs that allow retracing and reproducing any computed result. When an AiiDA database is shared on Materials Cloud, peers can browse the interconnected record of simulations, download individual files or the full database, and start their research from the results of the original authors. The infrastructure is agnostic to the specific simulation codes used and can support diverse applications in computational science that transcend its initial materials domain.

Weixian Xi, T. F. Scott, Christopher J. Kloxin et al.

Muhammad Qamar, Ghulam Abbas, Meiyong Liao et al.

Abstract Control over plasmonic properties and local electric field enhancement has become an essential aspect of many modern technologies. Here we investigate these phenomena in graphene / hexagonal boron nitride (G/h-BN) heterostructures positioned on silicon (Si) and silicon dioxide (SiO2) substrates. Using finite element method for physics-based simulations of radio-frequency (RF) fields in optical range, we analyze electric field at the edges, on the flakes, and in the surrounding regions of the G/h-BN heterostructures. The results demonstrate that the electric field distribution around and within the heterostructure is strongly dependent on the thickness of graphene and h-BN flakes. The highest electric field amplification and focusing occurs at the G/h-BN edge for h-BN thicknesses between 80 and 100 nm on the Si substrate. In contrast, the SiO2 substrate substantially reduces overall field intensity in the G/h-BN heterostructures in comparison to the Si and reference structure without h-BN. These findings provide a consistent theoretical explanation for previously reported experimental Raman spectroscopy data on G/h-BN heterostructures and corroborate the model of localized charge carrier accumulation at the nanoscale G/h-BN edges on Si substrates. Furthermore, the study provides predictions for optimal excitation frequencies and for tailoring graphene plasmonic features in visible spectral range with the use of diamond and other CMOS compatible materials.

Kyle L. Seyler, Giancarlo Soavi, Bent Weber et al.

Valleytronics exploits non-equivalent energy extrema in the electronic band structure of crystalline solids -- the valley degree of freedom -- to encode, manipulate, and read out information. The advent of 2D materials, first graphene and then transition-metal dichalcogenides, made valley control practical through optical, electrical, and magnetic routes. This foundation has enabled remarkable progress in recent years spanning established frontiers, such as valley exciton physics and valley Hall effects, as well as emerging directions including lightwave valleytronics, nanophotonic integration, flat-band valleytronics, and spin-valley qubits. In parallel, there are sustained efforts to scale up valleytronic materials and to predict new valleytronic platforms. This Roadmap brings together perspectives from leading experts to chart the key opportunities and challenges at the forefront of 2D material valleytronics. Each section captures a snapshot of progress in a key research area, identifies critical open challenges, and outlines pathways toward future valleytronics breakthroughs.

Camilo E. Calderon, J. Plata, C. Toher et al.

The Automatic-Flow (AFLOW) standard for the high-throughput construction of materials science electronic structure databases is described. Electronic structure calculations of solid state materials depend on a large number of parameters which must be understood by researchers, and must be reported by originators to ensure reproducibility and enable collaborative database expansion. We therefore describe standard parameter values for k-point grid density, basis set plane wave kinetic energy cut-off, exchange–correlation functionals, pseudopotentials, DFT+U parameters, and convergence criteria used in AFLOW calculations.

Ha Eun Kang, Seong-Do Kim, Young Soo Yoon et al.

Nickel-rich layered oxide cathodes, typified by compositions such as LiNi₁₋ₓ₋ᵧCoₓMnᵧO₂ (NCM) have garnered significant attention as high-energy-density candidates for next-generation lithium-ion batteries. However, their widespread deployment is hindered by a confluence of structural degradation, surface instability, and poor interfacial compatibility under high voltage cycling. To address these multifaceted limitations, this review comprehensively examines recent advances in surface coating and bulk doping strategies, which have emerged as pivotal approaches for enhancing the electrochemical stability and longevity of Ni-rich cathodes. Surface coatings including oxides, phosphates, and fluorides have been shown to effectively mitigate electrolyte-induced parasitic reactions and reinforce cathode–electrolyte interfaces. Simultaneously, elemental doping at transition-metal, lithium, and oxygen sites offer promising pathways to suppress cation disorder, stabilize layered frameworks, and facilitate Li⁺ transport. Emphasis is placed on site-specific doping mechanisms, the role of multi-site (co-)doping, and their synergistic interplay with surface modification layers. By synthesizing recent findings, this review delineates how the judicious integration of coating and doping techniques can enable the rational design of Ni-rich cathodes with enhanced structural integrity, rate capability, and cycle life.