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Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are attractive methods for obtaining quantum simulation data at longer time and length scales than possible with standard approaches. However, application of…

材料科学 · 物理学 2023-04-26 Nir Goldman , Laurence E. Fried , Rebecca K. Lindsey , C. Huy Pham , R. Dettori

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

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…

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

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

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

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 investigate the feasability of improving the semi-empirical density functional based tight-binding method (DFTB) through a general and transferable many-body repulsive potential for pure silicon using a common machine-learning framework.…

材料科学 · 物理学 2022-02-23 Dylan Bissuel , Tristan Albaret , Thomas A. Niehaus

The mineral schreibersite, e.g., Fe$_3$P, is commonly found in iron-rich meteorites and could have served as an abiotic phosphorus source for prebiotic chemistry. However, atomistic calculations of its degradation chemistry generally…

材料科学 · 物理学 2024-11-12 Riccardo Dettori , Nir Goldman

We have developed Density Functional Tight Binding (DFTB) models for cerium that accurately predict both the electronic band structure and energetic ordering of different allotropes. We show that global optimization of the electronic…

材料科学 · 物理学 2026-05-06 Nir Goldman , Artem Samtsevych , Chiara Panosetti

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

As semiconductor technologies continue to scale down to the nanoscale, the efficient prediction of material properties becomes increasingly critical. The tight-binding (TB) method is a widely used semi-empirical approach that offers a…

材料科学 · 物理学 2025-11-27 In Jun Park , Kamal Choudhary

The increasing need to simulate the dynamics of photoexcited molecular and nanosystems in the sub-picosecond regime demands new efficient tools able to describe the quantum nature of matter at a low computational cost. By combining the…

Current neural networks for predictions of molecular properties use quantum chemistry only as a source of training data. This paper explores models that use quantum chemistry as an integral part of the prediction process. This is done by…

化学物理 · 物理学 2018-08-22 Haichen Li , Christopher Collins , Matteus Tanha , Geoffrey J. Gordon , David J. Yaron

Magnetite has attracted increasing attention in recent years due to its promising and diverse applications in biomedicine. Theoretical modelling can play an important role in understanding magnetite-based nanomaterials at the atomic scale…

材料科学 · 物理学 2019-03-27 Hongsheng Liu , Gotthard Seifert , Cristiana Di Valentin

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

Parameterized tight-binding models fit to first principles calculations can provide an efficient and accurate quantum mechanical method for predicting properties of molecules and solids. However, well-tested parameter sets are generally…

材料科学 · 物理学 2023-04-28 Kevin F. Garrity , Kamal Choudhary

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

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

We present a new approach to studying nanoparticle collisions using Density Functional based Tight Binding (DFTB). A novel DFTB parameterisation has been developed to study the collision process of Sn and Si nanoparticles (NPs) using…

介观与纳米尺度物理 · 物理学 2023-08-03 Andrés Ruderman , M. B. Oviedo , S. A. Paz , E. P. M. Leiva
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