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Related papers: Ab initio Low-Dimensional Physics Opened Up by Dim…

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One-dimensional quantum optical models usually rest on the intuition of large scale separation or frozen dynamics associated with the different spatial dimensions, for example when studying quasi one-dimensional atomic dynamics, potentially…

Quantum Physics · Physics 2024-03-28 Jannik Ströhle , Richard Lopp

Motivated by the recent discovery of superconductivity in the iron-based ladder compound BaFe$_2$S$_3$ under high pressure, we derive low-energy effective Hamiltonians from first principles. We show that the complex band structure around…

Superconductivity · Physics 2015-08-28 Ryotaro Arita , Hiroaki Ikeda , Shiro Sakai , Michi-To Suzuki

Two-dimensional (2D) materials are particularly attractive to build the channel of next-generation field-effect transistors (FETs) with gate lengths below 10-15 nm. Because the 2D technology has not yet reached the same level of maturity as…

Mesoscale and Nanoscale Physics · Physics 2023-10-30 Mathieu Luisier , Cedric Klinkert , Sara Fiore , Jonathan Backman , Youseung Lee , Christian Stieger , Áron Szabó

An effective low-energy model describing magnetic properties of alkali-cluster-loaded sodalites is derived by {\em ab initio} downfolding. We start with constructing an extended Hubbard model for maximally localized Wannier functions. {\em…

Strongly Correlated Electrons · Physics 2010-10-20 Kazuma Nakamura , Takashi Koretsune , Ryotaro Arita

Correcting for detector effects in experimental data, particularly through unfolding, is critical for enabling precision measurements in high-energy physics. However, traditional unfolding methods face challenges in scalability,…

Data Analysis, Statistics and Probability · Physics 2024-11-28 Camila Pazos , Shuchin Aeron , Pierre-Hugues Beauchemin , Vincent Croft , Zhengyan Huan , Martin Klassen , Taritree Wongjirad

In this paper, we demonstrate a computationally efficient new approach based on deep learning (DL) techniques for analysis, design, and optimization of electromagnetic (EM) nanostructures. We use the strong correlation among features of a…

Machine Learning · Computer Science 2020-02-13 Yashar Kiarashinejad , Sajjad Abdollahramezani , Ali Adibi

The construction of predictive models of atomic nuclei from first principles is a challenging (yet necessary) task towards the systematic generation of theoretical predictions (and associated uncertainties) to support nuclear data…

Nuclear Theory · Physics 2024-07-26 Mikael Frosini , Thomas Duguet , Pierre Tamagno , Lars Zurek

We develop a novel deep learning technique, termed Deep Orthogonal Decomposition (DOD), for dimensionality reduction and reduced order modeling of parameter dependent partial differential equations. The approach consists in the construction…

Numerical Analysis · Mathematics 2024-05-15 Nicola Rares Franco , Andrea Manzoni , Paolo Zunino , Jan S. Hesthaven

A low-profile, modified Complementary Frequency Selective Surface (CFSS) with dual band-pass characteristic is presented. This technique adds independent control of the operation bands, which was a limitation from previous FSSs design based…

Systems and Control · Electrical Eng. & Systems 2022-11-15 Komlan Payne

We propose a multifidelity dimension reduction method to identify a low-dimensional structure present in many engineering models. The structure of interest arises when functions vary primarily on a low-dimensional subspace of the…

Numerical Analysis · Mathematics 2020-01-08 Rémi Lam , Olivier Zahm , Youssef Marzouk , Karen Willcox

We apply the Proper Orthogonal Decomposition (POD) method for the efficient simulation of several scenarios undergone by Micro-Electro-Mechanical-Systems, involving nonlinearites of geometric and electrostatic nature. The former type of…

Numerical Analysis · Mathematics 2022-02-22 Gobat G. , Opreni A. , Fresca S. , Manzoni A. , Frangi A

The quest to understand, design, and synthesize new forms of quantum matter guides much of contemporary research in condensed matter physics. One-dimensional (1D) electronic systems form the basis for some of the most interesting and exotic…

Strongly Correlated Electrons · Physics 2020-12-16 Megan Briggeman , Jianan Li , Mengchen Huang , Hyungwoo Lee , Jung-Woo Lee , Kitae Eom , Chang-Beom Eom , Patrick Irvin , Jeremy Levy

Amorphous insulating oxides play a significant role in the contemporary electronic industry. Understanding the band alignment of heterogeneous interfaces containing amorphous structures helps to better control the carrier transport property…

Materials Science · Physics 2017-05-17 Jianqiu Huang , Fei Lin , Celine Hin

Low-dimensional embeddings (LDEs) of high-dimensional data are ubiquitous in science and engineering. They allow us to quickly understand the main properties of the data, identify outliers and processing errors, and inform the next steps of…

Machine Learning · Computer Science 2024-06-17 Jonas Fischer , Rong Ma

The first part of this article centers on the fact that key features of the dynamical response of weakly-correlated materials (the alkalis, Al), have been found experimentally to differ qualitatively from simple-model behavior. In the…

Strongly Correlated Electrons · Physics 2007-05-23 Adolfo G. Eguiluz , Wei Ku

Correlations between electrons and the effective dimensionality are crucial factors that shape the properties of an interacting electron system. For example, the onsite Coulomb repulsion, U, may inhibit, or completely block the intersite…

Strongly Correlated Electrons · Physics 2007-05-23 T. Valla , P. D. Johnson , Z. Yusof , B. Wells , Q. Li , S. M. Loureiro , R. J. Cava , M. Mikami , Y. Mori , M. Yoshimura , T. Sasaki

Full-dimensional (FD) multi-user massive multiple input multiple output (m-MIMO) systems employ large two-dimensional (2D) rectangular antenna arrays to control both the azimuth and elevation angles of signal transmission. We introduce the…

Information Theory · Computer Science 2023-02-21 W. Zhu , H. D. Tuan , E. Dutkiewicz , Y. Fang , L. Hanzo

The iron-based LaFeAsO$_{1-x}$F$_x$ recently discovered by Hosono's group is a fresh theoretical challenge as a new class of high-temperature superconductors. Here we describe the electronic structure of the material and the mechanism of…

Superconductivity · Physics 2009-11-13 Hideo Aoki

We present a novel methodology to compute relaxed dislocations core configurations, and their energies in crystalline metallic materials using large-scale \emph{ab-intio} simulations. The approach is based on MacroDFT, a coarse-grained…

Computational Physics · Physics 2020-02-19 Mauricio Ponga , Kaushik Bhattacharya , Michael Ortiz

We examine emergent properties of 2D supramolecular networks, using enumeration of configurations formed by interacting dominoes on square lattices as a simple model system. Possible ground states are identified using a convex hull…

Soft Condensed Matter · Physics 2017-02-08 Joel Nicholls , Gareth P. Alexander , David Quigley