English
Related papers

Related papers: Predicting phase preferences of two-dimensional tr…

200 papers

High-throughput density-functional calculations of solids are extremely time consuming. As an alternative, we here propose a machine learning approach for the fast prediction of solid-state properties. To achieve this, LSDA calculations are…

Materials Science · Physics 2014-05-23 K. T. Schütt , H. Glawe , F. Brockherde , A. Sanna , K. R. Müller , E. K. U. Gross

Two-dimensional (2D) materials enable new types of magnetic and electronic phases mediated by their reduced dimensionality like magic-angle induced phase transitions, 2D Ising antiferromagnets and ferromagnetism in 2D atomic layers and…

Transition metal dichalcogenides (TMDs) attract significant attention due to their exceptional optical, excitonic, mechanical, and electronic properties. Nanostructured multilayer TMDs were recently shown to be highly promising for…

Two-dimensional transition metal dichalcogenides (TMDs) for advanced logic transistor technologies are deposited by various modifications of the chemical vapor deposition (CVD) method using a wide variety of precursors. Being a major…

Materials Science · Physics 2024-03-13 Vladislav Voronenkov , Benjamin Groven , Henry Medina Silva , Pierre Morin , Stefan De Gendt

The surface properties of solid-state materials often dictate their functionality, especially for applications where nanoscale effects become important. The relevant surface(s) and their properties are determined, in large part, by the…

Materials Science · Physics 2024-03-19 Kyle Noordhoek , Christopher J. Bartel

Recently, machine learning has emerged as an alternative, powerful approach for predicting quantum-mechanical properties of molecules and solids. Here, using kernel ridge regression and atomic fingerprints representing local environments of…

Materials Science · Physics 2018-03-08 Teppei Suzuki , Ryo Tamura , Tsuyoshi Miyazaki

Two-dimensional (2D) semiconducting transition metal dichalcogenides (TMDs) can withstand a large deformation without fracture or inelastic relaxation, making them attractive for application in novel strain-engineered and flexible…

Materials Science · Physics 2016-01-18 Junwen Li , Nikhil V. Medhekar , Vivek B. Shenoy

Atomically thin transition metal dichalcogenides (TMDs) hold promising potential for applications in optoelectronics. Due to their direct band gap and the extraordinarily strong Coulomb interaction, TMDs exhibit efficient light-matter…

Materials Science · Physics 2018-01-23 Maja Feierabend , Ermin Malic , Andreas Knorr , Gunnar Berghäuser

Due to their physical properties and potential applications in energy conversion and storage, transition metal dichalcogenides (TMDs) have garnered substantial interest in recent years. Amongst this class of materials, TMDs based on…

Two-dimensional (2D) semiconducting transition metal dichalcogenides (TMDCs) and black phosphorus (BP) have beneficial electronic, optical, and physical properties at the few-layer limit. As atomically thin materials, 2D TMDCs and BP are…

Materials Science · Physics 2016-03-30 Christopher R. Ryder , Joshua D. Wood , Spencer A. Wells , Mark C. Hersam

Two-dimensional (2D) transition metal dichalcogenide (TMD) materials have versatile electronic and optical properties. TMD nanoribbons show interesting properties due to reduced dimensionality, quantum confinement, and edge states. Tang et…

Materials Science · Physics 2023-06-16 Santosh Neupane , Hong Tang , Adrienn Ruzsinszky

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…

A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications,…

Materials Science · Physics 2016-08-29 Logan Ward , Ankit Agrawal , Alok Choudhary , Christopher Wolverton

Recently, there has been an increased interest in the application of machine learning (ML) techniques to a variety of problems in condensed matter physics. In this regard, of particular significance is the characterization of simple and…

Strongly Correlated Electrons · Physics 2023-11-22 F. A. Gómez Albarracín , H. D. Rosales

Two-dimensional lead halide perovskites are promising materials for optoelectronics due to the tunability of their properties with the number of lead halide layers and the choice of an organic spacer. Physical understanding for the rational…

Recent years have witnessed a growing interest in using machine learning to predict and identify phase transitions in various systems. Here we adopt convolutional neural networks (CNNs) to study the phase transitions of Vicsek model,…

Biological Physics · Physics 2023-06-27 Tingting Xue , Xu Li , Xiaosong Chen , Li Chen , Zhangang Han

As machine learning becomes increasingly important in engineering and science, it is inevitable that machine learning techniques will be applied to the investigation of materials, and in particular the structural phase transitions common in…

Materials Science · Physics 2021-03-30 Jiale Zhang , Danni Wei , Feng Zhang , Xi Chen , Dawei Wang

Single layer (SL) two-dimensional transition metal dichalcogenides (TMDs), such as MoS2, ReS2, WSe2, and MoTe2 have now become the focus of intensive fundamental and applied researches due to their intriguing and tunable physical…

The interest in obtaining high-quality monolayer transition metal dichalcogenides (TMDs) for optoelectronic device applications has been growing dramatically. However, the prevalence of defects and unwanted doping in these materials remains…

Chemical Physics · Physics 2023-01-18 Zhaojun Li , Hope Bretscher , Akshay Rao

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
‹ Prev 1 3 4 5 6 7 10 Next ›