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Transition metal dichalcogenides (TMDs) are interesting for understanding fundamental physics of two-dimensional materials (2D) as well as for many emerging technologies, including spin electronics. Here, we report the discovery of…

Modification of physical properties of materials and design of materials with on-demand characteristics is at the heart of modern technology. Rare application relies on pure materials--most devices and technologies require careful design of…

The large-scale search for high-performing candidate 2D materials is limited to calculating a few simple descriptors, usually with first-principles density functional theory calculations. In this work, we alleviate this issue by extending…

Materials Science · Physics 2020-07-07 Victor Venturi , Holden Parks , Zeeshan Ahmad , Venkatasubramanian Viswanathan

Considering the growing interest in magnetic materials for unconventional computing, data storage, and sensor applications, there is active research not only on material synthesis but also characterisation of their properties. In addition…

We introduce scalable machine learning models to accurately predict two key quantum transport properties, the transmission coefficient T(E) and average local density of states (Average-LDOS) in two-dimensional (2D) hexagonal materials with…

Mesoscale and Nanoscale Physics · Physics 2026-02-17 Seyed Mahdi Mastoor , Amirhossein Ahmadkhan Kordbacheh

The advent of computational statistical disciplines, such as machine learning, is leading to a paradigm shift in the way we conceive the design of new compounds. Today computational science does not only provide a sound understanding of…

Materials Science · Physics 2019-11-07 Alessandro Lunghi , Stefano Sanvito

Driven by applications in information technology, the search for new materials with stable, long-range magnetic ordering continues. Metalorganic magnets, involving the coordination of metal atoms with specific organic ligands, are a focus…

Recently supervised machine learning has been ascending in providing new predictive approaches for chemical, biological and materials sciences applications. In this Perspective we focus on the interplay of machine learning algorithm with…

Certain two-dimensional (2D) materials exhibit intriguing properties such as valley polarization, ferroelectricity, superconductivity and charge-density waves. Many of these materials can be manually assembled into atomic-scale multilayer…

Materials Science · Physics 2019-07-24 Jiangtan Yuan , Andrew Balk , Hua Guo , Sahil Patel , Xuanhan Zhao , Qiyi Fang , Douglas Natelson , Scott Crooker , Jun Lou

Magnetic ordering, as one of the most important characteristics in magnetic materials, could have significant influence on the band structure, spin dependent transport, and other important properties of materials. Its measurement,…

Materials Science · Physics 2015-08-18 Wen-Yi Tong , Hang-Chen Ding , Shi-jing Gong , Xiangang Wan , Chun-Gang Duan

The discovery of two-dimensional (2D) magnetism within atomically thin structures derived from layered crystals has opened up a new realm for exploring magnetic heterostructures. This emerging field provides a foundational platform for…

Mesoscale and Nanoscale Physics · Physics 2023-11-08 Bingyu Zhang , Pengcheng Lu , Roozbeh Tabrizian , Philip X. -L. Feng , Yingying Wu

The unique optical and electronic properties of two-dimensional transition metal dichalcogenides (2D TMDs) make them promising materials for applications in (opto-)electronics, catalysis and more. Specifically, alloys of 2D TMDs have broad…

Technologies that function at room temperature often require magnets with a high Curie temperature, $T_\mathrm{C}$, and can be improved with better materials. Discovering magnetic materials with a substantial $T_\mathrm{C}$ is challenging…

Materials Science · Physics 2023-08-09 Joshua F. Belot , Valentin Taufour , Stefano Sanvito , Gus L. W. Hart

Deep learning models are widely used for the data-driven design of materials based on atomic force microscopy (AFM) and other scanning probe microscopy. These tools enhance efficiency in inverse design and characterization of materials.…

Materials Science · Physics 2024-12-12 Isaiah A. Moses , Wesley F. Reinhart

An unconventional magnet may be mapped onto a simple ferromagnet by the existence of a high-symmetry point. Knowledge of conventional ferromagnetic systems may then be carried over to provide insight into more complex orders. Here we…

Computational Physics · Physics 2021-07-28 Nihal Rao , Ke Liu , Lode Pollet

Machine learning is applied to a large number of modern devices that are essential in building energy efficient smart society. Audio and face recognition are among the most well-known technologies that make use of such artificial…

Mesoscale and Nanoscale Physics · Physics 2021-02-02 Masashi Kawaguchi , Kenji Tanabe , Keisuke Yamada , Takuya Sawa , Shun Hasegawa , Masamitsu Hayashi , Yoshinobu Nakatani

We investigate the existence of linear magnetism in the metal organic framework materials MOF-74-Fe, MOF-74-Co, and MOF-74-Ni, using first-principles density functional theory. MOF-74 displays regular quasi-linear chains of open-shell…

Materials Science · Physics 2013-03-08 Pieremanuele Canepa , Yves J. Chabal , T. Thonhauser

Spontaneous magnetic order is a routine instance in three-dimensional (3D) materials but for a long time, it remained elusive in the 2D world. Recently, the first examples of (stand-alone) 2D van der Waals (vdW) crystals with magnetic…

Materials Science · Physics 2021-05-05 M. Blei , J. L. Lado , Q. Song , D. Dey , O. Erten , V. Pardo , R. Comin , S. Tongay , A. S. Botana

Our experimental (neutron diffraction, M\"ossbauer spectroscopy, magnetic susceptibility, specific heat) and numerical studies on the evolution of short- and long-range magnetic order in $\gamma_{\rm II}$-Li\(_2\)FeSiO\(_4\) suggest a…

Strongly Correlated Electrons · Physics 2025-01-20 W. Hergett , N. Bouldi , M. Jonak , C. Neef , C. Ritter , M. Abdel-Hafiez , F. Seewald , H. -H. Klauss , M. W. -Haverkort , R. Klingeler

We propose a novel representation of crystalline materials named orbital-field matrix (OFM) based on the distribution of valence shell electrons. We demonstrate that this new representation can be highly useful in mining material data. Our…