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Bridging the gap between first principles methods and empirical schemes, the density functional based tight-binding method (DFTB) has become a versatile tool in predictive atomistic simulations over the past years. One of the major…

材料科学 · 物理学 2015-04-02 Vitalij Lutsker , Balint Aradi , Thomas A. Niehaus

Density Functional Tight-Binding (DFTB), an approximative approach derived from Density Functional Theory (DFT), has the potential to pave the way for simulations of large periodic or non-periodic systems. We have specifically tailored DFTB…

The self-consistent charge density functional tight-binding (DFTB) theory is a useful tool for realizing the electronic structures of large molecular complex systems. In this study, we analyze the electronic structure of C61, formed by…

材料科学 · 物理学 2016-06-14 Jia Wang , Xing Dai , Wanrun Jiang , Tianrong Yu , Zhigang Wang

Routine investigations of plasmonic phenomena at the quantum level present a formidable computational challenge due to the large system sizes and ultrafast timescales involved. This Feature Article highlights the use of density functional…

介观与纳米尺度物理 · 物理学 2025-06-19 Nikhil S. Chellam , Subhajyoti Chaudhuri , Abhisek Ghosal , Sajal K. Giri , George C. Schatz

Using the density-functional-based tight-binding method we performed a systematic comparative study of stability, structural and electronic properties for 12 various types of graphene allotropes, which are likely candidates for engineering…

材料科学 · 物理学 2015-05-20 A. N. Enyashin , A. L. Ivanovskii

We develop a formalism to describe electron ejections from graphene-like targets by dark matter (DM) scattering for general forms of scalar and spin 1/2 DM-electron interactions and compare their applicability and accuracy within the…

高能物理 - 唯象学 · 物理学 2023-03-29 Riccardo Catena , Timon Emken , Marek Matas , Nicola A. Spaldin , Einar Urdshals

We combine density-functional tight-binding (DFTB) with deep tensor neural networks (DTNN) to maximize the strengths of both approaches in predicting structural, energetic, and vibrational molecular properties. The DTNN is used to learn a…

化学物理 · 物理学 2020-06-19 Martin Stöhr , Leonardo Medrano Sandonas , Alexandre Tkatchenko

This article is a pedagogical introduction to density-functional tight-binding (DFTB) method. We derive it from the density-functional theory, give the details behind the tight-binding formalism, and give practical recipes for…

材料科学 · 物理学 2009-11-02 Pekka Koskinen , Ville Mäkinen

We propose a new method of calculating electronically excited states that combines a density functional theory (DFT) based ground state calculation with a linear response treatment that employs approximations used in the time-dependent…

化学物理 · 物理学 2016-05-11 Robert Rüger , Erik van Lenthe , Thomas Heine , Lucas Visscher

Density Functional Tight Binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard Density Functional Theory approaches. However, DFTB models…

Over many years, computational simulations based on Density Functional Theory (DFT) have been used extensively to study many different materials at the atomic scale. However, its application is restricted by system size, leaving a number of…

介观与纳米尺度物理 · 物理学 2018-12-05 Carlos Romero-Muñiz , Ayako Nakata , Pablo Pou , David R. Bowler , Tsuyoshi Miyazaki , Rubén Pérez

Comparative molecular dynamics simulations of a hexamer cluster of the protic ionic liquid ethylammonium nitrate are performed using density functional theory (DFT) and density functional-based tight binding (DFTB) methods. The focus is on…

化学物理 · 物理学 2017-03-24 Tobias Zentel , Oliver Kühn

A generalization of the density-functional based tight-binding method (DFTB) for the use with range-separated exchange-correlation functionals is presented. It is based on the Generalized Kohn-Sham (GKS) formalism and employs the density…

材料科学 · 物理学 2015-06-03 Thomas A. Niehaus , Fabio Della Sala

We calculate the phonon-dispersion relations of several two-dimensional materials and diamond using the density-functional based tight-binding approach (DFTB). Our goal is to verify if this numerically efficient method provides sufficiently…

A derivation of non-adiabatic coupling vectors for the density functional based tight binding method (DFTB) between ground and excited states is presented. The analytical result is valid both for semi-local and long-range corrected DFTB and…

化学物理 · 物理学 2020-10-29 Thomas A. Niehaus

We compare the classification of the electron bands in graphene, obtained by group theory algebra in the framework of tight-binding model (TBM), with that calculated in the density-functional theory (DFT) framework. Identification in the…

介观与纳米尺度物理 · 物理学 2014-05-01 E. Kogan , V. U. Nazarov , V. M. Silkin , M. Kaveh

The time-dependent density functional based tight-binding (TD-DFTB) approach is generalized to account for fractional occupations. In addition, an on-site correction leads to marked qualitative and quantitative improvements over the…

材料科学 · 物理学 2013-02-15 A. Domínguez , B. Aradi , T. Frauenheim , V. Lutsker , T. A. Niehaus

Graphene sheets with regular perforations, dubbed as antidot lattices, have theoretically been predicted to have a number of interesting properties. Their recent experimental realization with lattice constants below 100 nanometers stresses…

介观与纳米尺度物理 · 物理学 2013-05-29 Joachim A. Fuerst , Thomas G. Pedersen , Mads Brandbyge , Antti-Pekka Jauho

We present a comparative Density Functional Theory (DFT) and Density Functional Tight Binding (DFTB) study of geometries and electronic structures of arginine (Arg), arginine adsorbed on the anatase (101) surface of titania in several…

材料科学 · 物理学 2017-05-08 Wenxuan Li , Konstantinos Kotsis , Sergei Manzhos

We present a new velocity-gauge real-time, time-dependent density functional tight-binding (VG-rtTDDFTB) implementation in the open-source DFTB+ software package (https://dftbplus.org) for probing electronic excitations in large, condensed…

介观与纳米尺度物理 · 物理学 2024-05-24 Qiang Xu , Mauro Del Ben , Mahmut Sait Okyay , Min Choi , Khaled Z. Ibrahim , Bryan M. Wong
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