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Electrides, with their excess electrons distributed in crystal cavities playing the role of anions, exhibit a variety of unique properties which make these materials desirable for many applications in catalysis, nonlinear optics and…

Materials Science · Physics 2019-08-30 Qiang Zhu , Timofey Frolov , Kamal Choudhary

This study presents a prototype D-band waveguide orthomode transducer (OMT) fabricated using chemically etched brass platelets. This method offers a fast, cost-effective, and scalable approach for producing waveguide OMTs above 100 GHz,…

The increasing financial and environmental cost of many inorganic materials has motivated study into organic and "green" alternatives. However, most organic compounds contain a large number of atoms in the primitive unit cell, posing a…

Materials Science · Physics 2024-05-14 Alexander C. Tyner , Alexander V. Balatsky

A thorough in situ characterization of materials at extreme conditions is challenging, and computational tools such as crystal structural search methods in combination with ab initio calculations are widely used to guide experiments by…

Materials Science · Physics 2018-11-14 Maximilian Amsler , Vinay I. Hegde , Steven D. Jacobsen , Chris Wolverton

Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science. While machine learning has emerged in recent years as a new paradigm to provide rapid predictions of materials properties,…

Materials Science · Physics 2021-01-29 Chi Chen , Yunxing Zuo , Weike Ye , Xiangguo Li , Shyue Ping Ong

Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future thanks to their higher theoretical energy density and improved safety. However, their adoption is currently hindered by…

The discovery of new materials is essential for enabling technological advancements. Computational approaches for predicting novel materials must effectively learn the manifold of stable crystal structures within an infinite design space.…

The band structure and the Fermi surface of the recently discovered superconductor (EMIM)$_x$FeSe are studied within the density functional theory in the generalized gradient approximation. We show that the bands near the Fermi level are…

Superconductivity · Physics 2022-03-28 L. V. Begunovich , M. M. Korshunov

Topological materials (TMs) showcase intriguing physical properties defying expectations based on conventional materials, and hold promise for the development of devices with new functionalities. While several theoretically proposed TMs…

Mesoscale and Nanoscale Physics · Physics 2019-04-19 Feng Tang , Hoi Chun Po , Ashvin Vishwanath , Xiangang Wan

Crystalline phase structure is essential for understanding the performance and properties of a material. Therefore, this study identified and quantified the crystalline phase structure of a sample based on the diffraction pattern observed…

Materials Science · Physics 2023-09-27 Ryo Murakami , Yoshitaka Matsushita , Kenji Nagata , Hayaru Shouno , Hideki Yoshikawa

Operational modal analysis (OMA) aims at identifying the modal properties of a structure based on response data of the structure excited by ambient sources. Modal parameters of the ambient vibration structures consist of natural…

Systems and Control · Electrical Eng. & Systems 2020-10-20 M. R. Davoodi , B. Navayi neya , S. A. Mostafavian , S. R. Nabavian , GH. R. Jahangiry

The humanities, like many other areas of society, are currently undergoing major changes in the wake of digital transformation. However, in order to make collection of digitised material in this area easily accessible, we often still lack…

Information Retrieval · Computer Science 2021-03-23 Vuong M. Ngo , Sven Helmer , Nhien-An Le-Khac , M-Tahar Kechadi

We study theoretically two-dimensional single-crystalline sheets of semiconductors that form a honeycomb lattice with a period below 10 nm. These systems could combine the usual semiconductor properties with Dirac bands. Using atomistic…

Mesoscale and Nanoscale Physics · Physics 2015-02-19 E. Kalesaki , C. Delerue , C. Morais Smith , W. Beugeling , G. Allan , D. Vanmaekelbergh

Topological materials discovery has evolved at a rapid pace over the past 15 years following the identification of the first nonmagnetic topological insulators (TIs), topological crystalline insulators (TCIs), and 3D topological semimetals…

Low-dimensional materials have attracted significant attentions over the past decade. To discover new low-dimensional materials, high-throughout screening methods have been applied in different materials databases. For this purpose, the…

Materials Science · Physics 2022-02-09 Hao Gao , Junjie Wang , Zhaopeng Guo , Jian Sun

Battery safety is critical in applications ranging from consumer electronics to electric vehicles and aircraft, where undetected anomalies could trigger safety hazards or costly downtime. In this study, we present OSBAD as an open-source…

Machine Learning · Computer Science 2025-11-04 Mei-Chin Pang , Suraj Adhikari , Takuma Kasahara , Nagihiro Haba , Saneyuki Ohno

Using density functional theory (DFT), we investigate mechanical properties of a few 2D metal-organic frameworks (MOFs) and covalent-organic frameworks (COFs) having Dirac and flat bands. These porous materials have become a subject of…

Materials Science · Physics 2023-03-15 Priyadarshini Kapri , Takuto Kawakami , Mikito Koshino

We present a first-principles investigation of the electronic structure of the inversion-symmetry-broken spin-orbit-coupled metal candidate PbRe$_2$O$_6$. Our calculations reveal that the Fermi surfaces derived from the $d_{yz}$ and…

Materials Science · Physics 2026-04-29 Yuki Yanagi , Michi-To Suzuki

In order to analytically capture and identify peculiarities in the electronic structure of silicene, Weaire-Thorpe(WT) model, a standard model for treating three-dimensional (3D) silicon, is applied to silicene with the buckled 2D…

Mesoscale and Nanoscale Physics · Physics 2015-03-30 Y. Hatsugai , K. Shiraishi , H. Aoki

Materials property predictions have improved from advances in machine learning algorithms, delivering materials discoveries and novel insights through data-driven models of structure-property relationships. Nearly all available models rely…

Materials Science · Physics 2022-04-13 Yiqun Wang , Xiao-Jie Zhang , Fei Xia , Elsa A. Olivetti , Ram Seshadri , James M. Rondinelli