DSKO: Dancing through DFTB Parametrization
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