Hasil untuk "Materials Science"

Ross Taylor, Marcin Kardas, Guillem Cucurull et al.

Information overload is a major obstacle to scientific progress. The explosive growth in scientific literature and data has made it ever harder to discover useful insights in a large mass of information. Today scientific knowledge is accessed through search engines, but they are unable to organize scientific knowledge alone. In this paper we introduce Galactica: a large language model that can store, combine and reason about scientific knowledge. We train on a large scientific corpus of papers, reference material, knowledge bases and many other sources. We outperform existing models on a range of scientific tasks. On technical knowledge probes such as LaTeX equations, Galactica outperforms the latest GPT-3 by 68.2% versus 49.0%. Galactica also performs well on reasoning, outperforming Chinchilla on mathematical MMLU by 41.3% to 35.7%, and PaLM 540B on MATH with a score of 20.4% versus 8.8%. It also sets a new state-of-the-art on downstream tasks such as PubMedQA and MedMCQA dev of 77.6% and 52.9%. And despite not being trained on a general corpus, Galactica outperforms BLOOM and OPT-175B on BIG-bench. We believe these results demonstrate the potential for language models as a new interface for science. We open source the model for the benefit of the scientific community.

A. Thompson, H. Aktulga, R. Berger et al.

Abstract Since the classical molecular dynamics simulator LAMMPS was released as an open source code in 2004, it has become a widely-used tool for particle-based modeling of materials at length scales ranging from atomic to mesoscale to continuum. Reasons for its popularity are that it provides a wide variety of particle interaction models for different materials, that it runs on any platform from a single CPU core to the largest supercomputers with accelerators, and that it gives users control over simulation details, either via the input script or by adding code for new interatomic potentials, constraints, diagnostics, or other features needed for their models. As a result, hundreds of people have contributed new capabilities to LAMMPS and it has grown from fifty thousand lines of code in 2004 to a million lines today. In this paper several of the fundamental algorithms used in LAMMPS are described along with the design strategies which have made it flexible for both users and developers. We also highlight some capabilities recently added to the code which were enabled by this flexibility, including dynamic load balancing, on-the-fly visualization, magnetic spin dynamics models, and quantum-accuracy machine learning interatomic potentials. Program Summary Program Title: Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) CPC Library link to program files: https://doi.org/10.17632/cxbxs9btsv.1 Developer's repository link: https://github.com/lammps/lammps Licensing provisions: GPLv2 Programming language: C++, Python, C, Fortran Supplementary material: https://www.lammps.org Nature of problem: Many science applications in physics, chemistry, materials science, and related fields require parallel, scalable, and efficient generation of long, stable classical particle dynamics trajectories. Within this common problem definition, there lies a great diversity of use cases, distinguished by different particle interaction models, external constraints, as well as timescales and lengthscales ranging from atomic to mesoscale to macroscopic. Solution method: The LAMMPS code uses neighbor lists, parallel spatial decomposition, and parallel FFTs for long-range Coulombic interactions [1]. The time integration algorithm is based on the Stormer-Verlet symplectic integrator [2], which provides better stability than higher-order non-symplectic methods. In addition, LAMMPS supports a wide range of interatomic potentials, constraints, diagnostics, software interfaces, and pre- and post-processing features. Additional comments including restrictions and unusual features: This paper serves as the definitive reference for the LAMMPS code. References [1] S. Plimpton. Fast parallel algorithms for short-range molecular dynamics. J. Comp. Phys., 117:1–19, 1995. [2] L. Verlet. Computer experiments on classical fluids: I. Thermodynamical properties of Lennard–Jones molecules. Phys. Rev., 159:98–103, 1967.

Hideaki Kobayashi, A. Hayakawa, K.D. Kunkuma . A. Somarathne et al.

Abstract This paper focuses on the potential use of ammonia as a carbon-free fuel, and covers recent advances in the development of ammonia combustion technology and its underlying chemistry. Fulfilling the COP21 Paris Agreement requires the de-carbonization of energy generation, through utilization of carbon-neutral and overall carbon-free fuels produced from renewable sources. Hydrogen is one of such fuels, which is a potential energy carrier for reducing greenhouse-gas emissions. However, its shipment for long distances and storage for long times present challenges. Ammonia on the other hand, comprises 17.8% of hydrogen by mass and can be produced from renewable hydrogen and nitrogen separated from air. Furthermore, thermal properties of ammonia are similar to those of propane in terms of boiling temperature and condensation pressure, making it attractive as a hydrogen and energy carrier. Ammonia has been produced and utilized for the past 100 years as a fertilizer, chemical raw material, and refrigerant. Ammonia can be used as a fuel but there are several challenges in ammonia combustion, such as low flammability, high NOx emission, and low radiation intensity. Overcoming these challenges requires further research into ammonia flame dynamics and chemistry. This paper discusses recent successful applications of ammonia fuel, in gas turbines, co-fired with pulverize coal, and in industrial furnaces. These applications have been implemented under the Japanese ‘Cross-ministerial Strategic Innovation Promotion Program (SIP): Energy Carriers’. In addition, fundamental aspects of ammonia combustion are discussed including characteristics of laminar premixed flames, counterflow twin-flames, and turbulent premixed flames stabilized by a nozzle burner at high pressure. Furthermore, this paper discusses details of the chemistry of ammonia combustion related to NOx production, processes for reducing NOx, and validation of several ammonia oxidation kinetics models. Finally, LES results for a gas-turbine-like swirl-burner are presented, for the purpose of developing low-NOx single-fuelled ammonia gas turbine combustors.

Songyuan Ding, Jun Yi, Jian-feng Li et al.

David Weintrop, Elham Beheshti, Michael S. Horn et al.

A. Karpatne, G. Atluri, James H. Faghmous et al.

Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery. The overarching vision of TGDS is to introduce scientific consistency as an essential component for learning generalizable models. Further, by producing scientifically interpretable models, TGDS aims to advance our scientific understanding by discovering novel domain insights. Indeed, the paradigm of TGDS has started to gain prominence in a number of scientific disciplines such as turbulence modeling, material discovery, quantum chemistry, bio-medical science, bio-marker discovery, climate science, and hydrology. In this paper, we formally conceptualize the paradigm of TGDS and present a taxonomy of research themes in TGDS. We describe several approaches for integrating domain knowledge in different research themes using illustrative examples from different disciplines. We also highlight some of the promising avenues of novel research for realizing the full potential of theory-guided data science.

D. Cambié, C. Bottecchia, Natan J. W. Straathof et al.

Alexander A. Aarts, Joanna E. Anderson, Christopher J. Anderson et al.

N. Denzin, Y. Lincoln

Ke Wang, B. Achour

T. Ebbesen

M. Gjerding, Ali Taghizadeh, Asbjørn Rasmussen et al.

The Computational 2D Materials Database (C2DB) is a highly curated open database organising a wealth of computed properties for more than 4000 atomically thin two-dimensional (2D) materials. Here we report on new materials and properties that were added to the database since its first release in 2018. The set of new materials comprise several hundred monolayers exfoliated from experimentally known layered bulk materials, (homo)bilayers in various stacking configurations, native point defects in semiconducting monolayers, and chalcogen/halogen Janus monolayers. The new properties include exfoliation energies, Bader charges, spontaneous polarisations, Born charges, infrared polarisabilities, piezoelectric tensors, band topology invariants, exchange couplings, Raman spectra and second harmonic generation spectra. We also describe refinements of the employed material classification schemes, upgrades of the computational methodologies used for property evaluations, as well as significant enhancements of the data documentation and provenance. Finally, we explore the performance of Gaussian process-based regression for efficient prediction of mechanical and electronic materials properties. The combination of open access, detailed documentation, and extremely rich materials property data sets make the C2DB a unique resource that will advance the science of atomically thin materials.

Yibo Li, Zhongrui Wang, Rivu Midya et al.

The memristor is considered as the one of the promising candidates for next generation computing systems. Novel computing architectures based on memristors have shown great potential in replacing or complementing conventional computing platforms based on the von Neumann architecture which faces challenges in the big-data era such as the memory wall. However, there are a number of technical challenges in implementing memristor based computing. In this review, we focus on the research performed on the memristor material stacks and their compatibility with CMOS processes, the electrical performance, and the integration. In addition, recent demonstrations of neuromorphic computing using memristors are surveyed.

H. Karahan, Kunli Goh, C. Zhang et al.

MXenes are emerging rapidly as a new family of multifunctional nanomaterials with prospective applications rivaling that of graphenes. Herein, a timely account of the design and performance evaluation of MXene‐based membranes is provided. First, the preparation and physicochemical characteristics of MXenes are outlined, with a focus on exfoliation, dispersion stability, and processability, which are crucial factors for membrane fabrication. Then, different formats of MXene‐based membranes in the literature are introduced, comprising pristine or intercalated nanolaminates and polymer‐based nanocomposites. Next, the major membrane processes so far pursued by MXenes are evaluated, covering gas separation, wastewater treatment, desalination, and organic solvent purification. The potential utility of MXenes in phase inversion and interfacial polymerization, as well as layer‐by‐layer assembly for the preparation of nanocomposite membranes, is also critically discussed. Looking forward, exploiting the high electrical conductivity and catalytic activity of certain MXenes is put into perspective for niche applications that are not easily achievable by other nanomaterials. Furthermore, the benefits of simulation/modeling approaches for designing MXene‐based membranes are exemplified. Overall, critical insights are provided for materials science and membrane communities to navigate better while exploring the potential of MXenes for developing advanced separation membranes.

Hao Wang, Wang Zhang, D. Ladika et al.

The rapid development of additive manufacturing has fueled a revolution in various research fields and industrial applications. Among the myriad of advanced 3D printing techniques, two‐photon polymerization lithography (TPL) uniquely offers a significant advantage in nanoscale print resolution, and has been widely employed in diverse fields, for example, life sciences, materials sciences, mechanics, and microfluidics. More recently, by virtue of the optical transparency of most of the resins used, TPL is finding new applications in optics and photonics, with nanometer to millimeter feature dimensions. It enables the minimization of optical elements and systems, and exploration of light‐matter interactions with new degrees of freedom, never possible before. To review the recent progress in the TPL related optical research, it starts with the fundamentals of TPL and material formulation, then discusses novel fabrication methods, and a wide range of optical applications. These applications notably include diffractive, topological, quantum, and color optics. With a panoramic view of the development, it is concluded with insights and perspectives of the future development of TPL and related potential optical applications.

E. Song, Jinghua Li, S. Won et al.

Feifan Liu, Lun He, Lvlv Ji et al.

ABSTRACT Due to a positive standard reaction Gibbs free energy (ΔrGmθ) of 237.1 kJ mol−1, electric energy input is indispensable for hydrogen production by conventional electrochemical water splitting. This energy requirement can be reduced by replacing the anodic oxygen evolution reaction to thermodynamic favorable small‐molecules oxidation reactions. In this work, anodic formaldehyde oxidation reaction (FOR) in alkaline media was paired with cathodic hydrogen evolution reaction (HER) in acidic media to establish a thermodynamically downhill system. The utilization of electrochemical neutralization energy in a hybrid alkaline‐acidic electrolyte configuration enables a further decrease in ΔrGmθ. Therefore, the resulting hybrid alkaline‐acidic formaldehyde‐proton fuel cell (FPFC) exhibits a significantly reduced ΔrGmθ of −101.5 kJ mol−1. A bifunctional Ru‐doped Cu catalyst (Ru─Cu NTs@CM) was designed and synthesized to simultaneously promote the kinetics of acidic HER and alkaline FOR, demonstrating superior catalytic activity and durability to pristine Cu and Ru catalysts. This catalyst enabled concurrent bipolar H2 production and electricity generation from the assembled FPFC, reaching a peak power density of 18.3 mW cm−2 at 53.4 mA cm−2. A combination of (quasi) in situ characterizations and theoretical calculations unveiled the important mechanistic role of Ru‐doping in enhancing the Cu catalyst's activity and stability.

Zhiqiang Dai, Rungroj Chanajaree, Chengwu Yang et al.

Traditional aqueous electrolyte systems in zinc-ion batteries (ZIBs) often face challenges such as sluggish ion transfer kinetics, dendrite formation, and sudden battery failures in harsh temperature environments. Herein, we introduce a pioneering approach by integrating a bifunctional additive composed of ethylene glycol (EG) and sodium gluconate (Ga) into ZnSO4 (ZSO) electrolyte to overcome these obstacles. The polyhydroxy structures of EG and Ga can reconstruct the hydrogen bond network of H2O to improve its liquid stability, and also adjust the coordination environment around hydrated Zn2+. Additionally, Ga in the H2O–EG mixture leads to the formation of a robust protective layer that promotes uniform deposition of Zn2+ ions and minimizes unwanted side reactions. Therefore, Zn anodes with 40% ZSO–Ga electrolyte can cycle for more than 3,000 h at 25 °C and 800 h at 50 °C. Furthermore, Zn||NH4V4O10 (NVO) full batteries demonstrate remarkable cycle stability, lasting up to 10,000 cycles at 1 A g−1 with a capacity retention of 79.1%. The multifunctional electrolyte additive employed in this study emerges as a promising candidate for enabling highly stable zinc anodes under diverse temperature conditions.

Qingfeng Miao, Xiaoyu Liu, Haibin Shi et al.

Examining lake-area evolution and influencing factors is essential for understanding global environmental and societal changes and supporting ecological sustainability. Inner Mongolia, China, given its unique geographical and climatic conditions, serves as a natural laboratory for investigating the complex coupling mechanisms of “climate–hydrology–humanities.” Accordingly, we analyzed data regarding annual area changes in 655 lakes across five basins obtained from Landsat, Sentinel-2, and pushbroom multispectral scanner (1987–2023), combined with meteorological, hydrological, and human factors affecting lake-area changes. Results indicated that lake areas varied from 4059.36 to 6489.46 km2 in 1987–2023, exhibiting an overall decline of 38.06 km2/a (R2 = 0.39, p < 0.001). This trend was nonlinear, exhibiting area expansion (1987–1998), rapid shrinkage (1998–2010), and stabilization after a slight rebound (2010–2023). Natural factors dominated lake-area dynamics in the Songhua and Northwest River Basins, while human activities, particularly agriculture, were key drivers in the Liaohe, Haihe, and Yellow River Basins. These findings provide critical insights into the drivers of lake-area changes and establish a scientific basis for developing effective water-resource management and ecological protection strategies.

Farhin Tabassum, M. R. Asekin, M. Salim Kaiser et al.

The mechanical and frictional behaviors of jute-textile-reinforced polymer composites have been investigated experimentally under the influence of matrix modification and postprocessing thermal treatments. Three different matrix modifiers, namely, carbon, silicon, and aluminum powders are considered for the modification of polymeric material used in the sandwich structured biodegradable jute-textile-reinforced composites. The modified composites are then subjected to post-processing thermal treatments isochronally at temperatures within the range of 0–250°C. Inclusion of carbon, silicon, and aluminum powders into the polyester resin leads to significant changes in the performance of the composite materials in terms of hardness, tensile, as well as wear and frictional properties. More specifically, the aluminum powders show the most promising potential to improve the properties of polyester-jute composites compared with those of silicon and carbon powders. Moreover, at the postprocessing temperature of 125°C, all the modified composite samples show their best performances in terms of hardness, strength, modulus, wear rate, and friction coefficient, which is eventually verified to be the optimum postprocessing temperature for the composites of the present type. The pin-on-disc wear study shows that under constant and varying load conditions, the coefficient of friction of the composite is found to be relatively higher for the case of aluminum-powder modifier compared with those of silicon and carbon power modifiers. The results of optical microstructures, scanning electron microscopic (SEM) images, and energy dispersive x-ray (EDX) spectra are found to be in support of the results observed through direct measurements. A quantitative comparison of the measured results verifies the relative improvement of the major mechanical and frictional properties of the composites, which, in turn, verifies the effectiveness of the selective matrix modifiers in conjunction with post-thermal treatments.