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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…

Quantum-mechanical (QM) simulations, thanks to their predictive power, can provide significant insights into the nature and dynamics of defects such as vacancies, dislocations and grain boundaries. These considerations are essential in the…

Materials Science · Physics 2021-11-17 Bartosz Barzdajn , Alexander M Garrett , Thomas M Whiting , Christopher P Race

Semiconducting transition metal dichalcogenides present a complex electronic band structure with a rich orbital contribution to their valence and conduction bands. The possibility to consider the electronic states from a tight-binding model…

Materials Science · Physics 2016-11-15 J. A. Silva-Guillén , P. San-Jose , R. Roldán

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…

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…

Mesoscale and Nanoscale Physics · Physics 2023-08-03 Andrés Ruderman , M. B. Oviedo , S. A. Paz , E. P. M. Leiva

The formal link between the linear combination of atomic orbitals approach to density functional theory and two-center Slater-Koster tight-binding models is used to derive an orthogonal $d$-band tight-binding model for iron with only two…

Materials Science · Physics 2015-03-19 Georg K. H. Madsen , Eunan J. McEniry , Ralf Drautz

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…

Materials Science · Physics 2026-05-06 Nir Goldman , Artem Samtsevych , Chiara Panosetti

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.…

Materials Science · Physics 2022-02-23 Dylan Bissuel , Tristan Albaret , Thomas A. Niehaus

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…

We propose a five-band tight-binding model for the Fe-As layers of iron pnictides with the hopping amplitudes calculated within the Slater-Koster framework. The band structure found in DFT, including the orbital content of the bands, is…

Superconductivity · Physics 2009-10-08 M. J. Calderon , B. Valenzuela , E. Bascones

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…

Chemical Physics · Physics 2020-10-29 Thomas A. Niehaus

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…

Chemical Physics · Physics 2018-08-22 Haichen Li , Christopher Collins , Matteus Tanha , Geoffrey J. Gordon , David J. Yaron

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…

Materials Science · Physics 2016-06-14 Jia Wang , Xing Dai , Wanrun Jiang , Tianrong Yu , Zhigang Wang

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…

Chemical Physics · Physics 2020-06-19 Martin Stöhr , Leonardo Medrano Sandonas , Alexandre Tkatchenko

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…

Materials Science · Physics 2024-11-12 Riccardo Dettori , Nir Goldman

Density-functional based tight-binding is a powerful method to describe large molecules and materials. Metal-Organic Frameworks (MOFs), materials with interesting catalytic properties and with very large surface areas have been developed…

\textit{Ab initio} pseudo-atomic orbital (PAO) Hamiltonians express the electronic structure of a solid in a compact, localized basis that spans the same Hilbert space as a conventional Slater--Koster tight-binding model, thereby providing…

Materials Science · Physics 2026-04-17 Marco Buongiorno Nardelli

The halide perovskites have truly emerged as efficient optoelectronic materials and show the promise of exhibiting nontrivial topological phases. Since the bandgap is the deterministic factor for these quantum phases, here we present a…

Materials Science · Physics 2021-03-31 Ravi Kashikar , Mayank Gupta , B. R. K. Nanda

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…

Materials Science · Physics 2015-04-02 Vitalij Lutsker , Balint Aradi , Thomas A. Niehaus

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…

Materials Science · Physics 2019-03-27 Hongsheng Liu , Gotthard Seifert , Cristiana Di Valentin
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