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Related papers: Steels and Stainless Steels

200 papers

Polymeric materials are widely used in many applications and are especially useful when combined with other polymers to make polymer composites. The appealing features of these materials come from their having comparable levels of strength…

Applications · Statistics 2018-04-13 Caleb King , Zhibing Xu , I-Chen Lee , Yili Hong

Electrical iron silicon steel is the most commonly used soft magnetic material in electrical energy conversion and transmission, and its demand is expected to increase with the need for electrification of the transportation sector and the…

Materials Science · Physics 2023-02-09 Martin Heller , Nora Leuning , Marlies Reher , Kay Hameyer , Sandra Korte-Kerzel

Next-generation gravitational wave detectors (GWDs) such as the Cosmic Explorer and Einstein Telescope demand extensive ultra-high vacuum systems, making material cost and performance critical considerations. This study investigates the…

Instrumentation and Methods for Astrophysics · Physics 2025-04-29 Carlo Scarcia , Giuseppe Bregliozzi , Paolo Chiggiato , Ivo Wevers

Nowadays powerful X-ray sources like synchrotrons and free-electron lasers are considered as ultimate tools for probing microscopic properties in materials. However, the correct interpretation of such experiments requires a good…

Materials Science · Physics 2018-02-28 B. Ruta , F. Zontone , Y. Chushkin , G. Baldi , G. Pintori , G. Monaco , B. Rufflé , W. Kob

Progress in particle physics depends on a multitude of unique facilities and capabilities that enable to advance detector technologies. Among others, key facilities involve test beams and irradiation facilities, which allow users to test…

Accelerator Physics · Physics 2022-03-21 M. Hartz , P. Merkel , E. Niner , E. Prebys , N. Toro

Machine learning was utilized to efficiently boost the development of soft magnetic materials. The design process includes building a database composed of published experimental results, applying machine learning methods on the database,…

The application of machine learning in materials presents a unique challenge of dealing with scarce and varied materials data - both experimental and theoretical. Nevertheless, several state-of-the-art machine learning models for materials…

The apparently inimical relationship between magnetism and superconductivity has come under increasing scrutiny in a wide range of material classes, where the free energy landscape conspires to bring them in close proximity to each other.…

Strongly Correlated Electrons · Physics 2015-05-20 Sunil Nair , O. Stockert , U. Witte , M. Nicklas , R. Schedler , K. Kiefer , J. D. Thompson , A. D. Bianchi , Z. Fisk , S. Wirth , F. Steglich

The microscopic composition and properties of matter at super-saturation densities have been the subject of intense investigation for decades. The scarcity of experimental and observational data has lead to the necessary reliance on…

Solar and Stellar Astrophysics · Physics 2015-06-15 J. R. Stone

The microstructure critically governs the properties of materials used in energy and chemical engineering technologies, from catalysts and filters to thermal insulators and sensors. Therefore, accurate design is based on quantitative…

Computational Engineering, Finance, and Science · Computer Science 2026-01-29 Maksym Szemer , Szymon Buchaniec , Grzegorz Brus

Mechanical and elastic properties of materials are among the most fundamental quantities for many engineering and industrial applications. Here, we present a formulation that is efficient and accurate for calculating the elastic and bending…

Materials Science · Physics 2026-03-23 Changpeng Lin , Samuel Poncé , Francesco Macheda , Francesco Mauri , Nicola Marzari

The physics of two-dimensional (2D) materials and heterostructures based on such crystals has been developing extremely fast. With new 2D materials, truly 2D physics has started to appear (e.g. absence of long-range order, 2D excitons,…

Materials Science · Physics 2016-08-11 K. S. Novoselov , A. Mishchenko , A. Carvalho , A. H. Castro Neto

Machine learning (ML) has emerged as a powerful tool for accelerating the computational design and production of materials. In materials science, ML has primarily supported large-scale discovery of novel compounds using first-principles…

Atomically thin metallenes have properties attractive for applications, but they are intrinsically unstable and require delicate stabilization in pores or other nano-constrictions. Substrates provide solid support, but metallenes' wanted…

Materials Science · Physics 2025-06-30 Kameyab Raza Abidi , Pekka Koskinen

Thermoelectricity is a promising avenue for harvesting energy but large-scale applications are still hampered by the lack of highly-efficient low-cost materials. Recently, Fe$_2YZ$ Heusler compounds were predicted theoretically to be…

Materials Science · Physics 2019-11-21 Sébastien Lemal , Fabio Ricci , Daniel I. Bilc , Matthieu J. Verstraete , Philippe Ghosez

The behavior of nuclear matter is studied at low densities and temperatures using classical molecular dynamics with three different sets of potentials with different compressibility. Nuclear matter is found to arrange in crystalline…

Nuclear Theory · Physics 2013-05-13 C. O. Dorso , P. A. Giménez Molinelli , J. I. Nichols , J. A. López

Materials informatics offers a promising pathway towards rational materials design, replacing the current trial-and-error approach and accelerating the development of new functional materials. Through the use of sophisticated data analysis…

Materials Science · Physics 2018-05-17 Cormac Toher , Corey Oses , Stefano Curtarolo

Structural materials are broadly used in applications such as nuclear vessels, high-temperature processes, and civil construction. Usually, during their placing and lifespan, they may present free or chemically bonded liquid phases in their…

Materials Science · Physics 2021-03-29 M. H. Moreira , R. F. Ausas , S. Dal Pont , P. I. Pelissari , A. P. Luz , V. C. Pandolfelli

This paper presents the latest trends in the powering of particle accelerators. A series of solutions is proposed for responding to the challenges of high performance machines. This paper covers the domains of magnetic field uncertainty,…

Accelerator Physics · Physics 2016-07-07 J-P Burnet

Large-scale storage technologies are crucial to balance consumption and intermittent production of renewable energy systems. One of these technologies can be developed by converting the excess energy into compressed air or hydrogen, i.e.,…

Geophysics · Physics 2024-07-29 Hermínio Tasinafo Honório , Hadi Hajibeygi
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