DSKO: Dancing through DFTB Parametrization

Density functional tight binding (DFTB) offers a computationally efficient alternative to ab initio methods, bridging the accuracy of density functional theory (DFT) and the speed of semiempirical models. The approximate nature of DFTB makes its reliability highly dependent on parameter quality. While recent advancements have significantly improved the parametrization of the so-called repulsive potential, the parametrization of the electronic part of the DFTB interaction remains relatively simplistic and underdeveloped. We present DFTB Slater-Koster Optimizer (DSKO), a novel framework that aims at producing accurate and transferable electronic parameter sets under rigorous physical constraints. Incorporating robust optimization algorithms and physics-informed loss functions, DSKO generates DFTB electronic parameters that yield electronic properties, such as density of states and band structures, closely matching DFT reference data. The versatility of DSKO facilitates the wide application of DFTB to materials science challenges, paving the way for routine high-fidelity semiempirical simulations.

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Publication Details

Journal
Journal of Chemical Theory and Computation
Published
2026-05-01
DOI
https://doi.org/10.1021/acs.jctc.6c00121
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

DSKO: Dancing through DFTB Parametrization

Tammo van der Heide, Chiara Panosetti, Bálint Aradi, Yihua Song et al.
Journal of Chemical Theory and Computation
Machine Learning in Materials Science
article

DSKO: Dancing through DFTB Parametrization

Tammo van der Heide, Chiara Panosetti, Bálint Aradi, Yihua Song, Artem Samtsevych, Karsten Reuter, Benjamin Hourahine, Christoph Scheurer, Reinhard J Maurer
article en

Abstract

Density functional tight binding (DFTB) offers a computationally efficient alternative to ab initio methods, bridging the accuracy of density functional theory (DFT) and the speed of semiempirical models. The approximate nature of DFTB makes its reliability highly dependent on parameter quality. While recent advancements have significantly improved the parametrization of the so-called repulsive potential, the parametrization of the electronic part of the DFTB interaction remains relatively simplistic and underdeveloped. We present DFTB Slater-Koster Optimizer (DSKO), a novel framework that aims at producing accurate and transferable electronic parameter sets under rigorous physical constraints. Incorporating robust optimization algorithms and physics-informed loss functions, DSKO generates DFTB electronic parameters that yield electronic properties, such as density of states and band structures, closely matching DFT reference data. The versatility of DSKO facilitates the wide application of DFTB to materials science challenges, paving the way for routine high-fidelity semiempirical simulations.

Journal of Chemical Theory and Computation
Supélec (FR), University of Vienna (AT), University of Applied Sciences and Arts of Southern Switzerland (CH), University of Bremen (DE), University of Strathclyde (GB), Fritz Haber Institute of the Max Planck Society (DE), University of Warwick (GB), Sup Management (MA)
Bundesministerium für Bildung und Forschung
Openalex Percentile: Top 15%
Machine Learning in Materials Science
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DSKO: Dancing through DFTB Parametrization — Tammo van der Heide, Chiara Panosetti, et al. · Journal of Chemical Theory and Computation (2026) | TGRS Research Map | TGRS