Recent Developments in DFTB+, a Software Package for Efficient Atomistic Quantum Mechanical Simulations

DFTB+ is a flexible, open-source software package developed by its community, designed for fast and efficient atomistic quantum mechanical simulations. It employs various methods that approximate density functional theory (DFT), such as density functional-based tight binding (DFTB) and the extended tight binding (xTB) approach allowing simulations of large systems over extended time scales with reasonable accuracy, while being significantly faster than traditional ab initio methods. In recent years, several new extensions of the DFTB method have been developed and implemented in the DFTB+ program package in order to improve the accuracy and generality of the available simulation results. In this paper, we review those enhancements, show several use case examples and discuss the strengths and limitations of its features.

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Publication Details

Journal
The Journal of Physical Chemistry A
Published
2025-06-06
DOI
https://doi.org/10.1021/acs.jpca.5c01146
Citations
6
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
3.11

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article

Recent Developments in DFTB+, a Software Package for Efficient Atomistic Quantum Mechanical Simulations

Sebastian Ehlert, Christoph Scheurer, Thomas A. Niehaus, Stephan Irle et al.
6 citations
The Journal of Physical Chemistry A
Machine Learning in Materials Science
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article

Recent Developments in DFTB+, a Software Package for Efficient Atomistic Quantum Mechanical Simulations

Sebastian Ehlert, Christoph Scheurer, Thomas A. Niehaus, Stephan Irle, Megan Y. Deshaye, Alexander A. Maryewski, Karsten Reuter, Tomáš Kubař, Van‐Quan Vuong, Marcus Elstner, Tim Kowalczyk, Nir Goldman, In Seong Lee, Tobias Melson, Thomas Frauenheim, Carlos R. Lien-Medrano, Qiang Cui, Tammo van der Heide, Seung Kyu Min, B. Hourahine, Cristopher Camacho, Gonzalo Díaz Mirón, Matías Berdakin, Cristián G. Sánchez, Bálint Aradi, Franco P. Bonafé, Anders M. N. Niklasson, Michael A. Sentef, P. V. Stishenko, J. A. Bich, Alessandro Pecchia, Roberto Andrés González León
article en
6 citations

Abstract

DFTB+ is a flexible, open-source software package developed by its community, designed for fast and efficient atomistic quantum mechanical simulations. It employs various methods that approximate density functional theory (DFT), such as density functional-based tight binding (DFTB) and the extended tight binding (xTB) approach allowing simulations of large systems over extended time scales with reasonable accuracy, while being significantly faster than traditional ab initio methods. In recent years, several new extensions of the DFTB method have been developed and implemented in the DFTB+ program package in order to improve the accuracy and generality of the available simulation results. In this paper, we review those enhancements, show several use case examples and discuss the strengths and limitations of its features.

The Journal of Physical Chemistry AVol. 129(24)
Université Claude Bernard Lyon 1 (FR), Karlsruhe Institute of Technology (DE), Boston University (US), The Abdus Salam International Centre for Theoretical Physics (ICTP) (IT), Lawrence Livermore National Laboratory (US), Oak Ridge National Laboratory (US), Centre National de la Recherche Scientifique (FR), Los Alamos National Laboratory (US), Consejo Nacional de Investigaciones Científicas y Técnicas (AR), Universidad Nacional de Cuyo (AR), Universität Hamburg (DE), Universidad Nacional de Córdoba (AR), University of Bremen (DE), University of Strathclyde (GB), Constructor University (DE), Fritz Haber Institute of the Max Planck Society (DE), Constructing Excellence (GB), University of California System (US), Universidad de Costa Rica (CR), Instituto Interdisciplinario de Ciencias Básicas (AR), Institute of Nanostructured Materials (IT), Instituto de Investigaciones en Ciencias de la Salud (AR), Institute for Basic Science (KR), Microsoft (Netherlands) (NL), Instituto de Física Teórica (ES), Centro Laser (IT), Yukhnovskii Institute for Condensed Matter Physics of the National Academy of Sciences of Ukraine (UA), Microsoft Research (India) (IN), Centro Científico Tecnológico - Tucumán (AR), Division of Materials Science and Engineering (AU), Max Planck Computing and Data Facility (DE), Institut Lumière Matière (FR), Center for Free-Electron Laser Science (DE), Max Planck Institute for the Structure and Dynamics of Matter (DE), University of Wales Institute Cardiff (SG), Ulsan National Institute of Science and Technology (KR), Western Washington University (US), Cardiff University (GB), Cardiff Metropolitan University (GB), University of California, Davis (US), University of Wales (GB)
National Science Foundation, Camille and Henry Dreyfus Foundation, UK Research and Innovation, Deutsche Forschungsgemeinschaft, Consejo Nacional de Investigaciones Científicas y Técnicas, National Institutes of Health, Secretaria de Ciencia y Tecnología - Universidad Nacional de Córdoba, European Research Council, Basic Energy Sciences, Lawrence Livermore National Laboratory, Los Alamos National Laboratory
Openalex Percentile: Top 8%
Machine Learning in Materials Science
3.11
DOIOpen Access PDFOpenAlex
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