DSKO: Dancing through DFTB Parametrization

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Authors: Artem Samtsevych, Yihua Song, Tammo van der Heide, Bálint Aradi, Benjamin Hourahine, Reinhard J Maurer, Karsten Reuter, Christoph Scheurer, Chiara Panosetti
Cited by: 1
References: 92

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.

Fields of Study & Subjects

Machine Learning in Materials Science Advanced Chemical Physics Studies Advanced Physical and Chemical Molecular Interactions

Publication details

Journal / source
Journal of Chemical Theory and Computation
Publisher
American Chemical Society (ACS)
Volume
22
Issue
9
Pages
4379–4393
Publication date
1 May 2026
Language
EN