ArticleJournal of chemical theory and computation2026
DSKO: Dancing through DFTB Parametrization.
Article in Journal of chemical theory and computation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Determination of Density Functional Tight Binding Models for Cerium-Containing Materials.Journal of chemical theory and computation · 2026Article
- Extensions to Extended Tight-Binding Methods for Transition-Metal Containing Systems.Journal of computational chemistry · 2026Article
- Optimizing Extended Tight-Binding Methods for Metal-Surface Interactions.Chemphyschem : a European journal of chemical physics and physical chemistry · 2025Article
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Authors and funding
9 authors.
Funding
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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.
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Registered trials
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