SlaKoNet: A Unified Slater-Koster Tight-Binding Framework Using Neural Network Infrastructure for the Periodic Table

Abstract

Accurate and efficient prediction of electronic band structures is essential for designing materials with targeted properties. However, existing machine learning models often lack universality and struggle to predict detailed electronic structures, while traditional tight-binding models based on the Slater-Koster (SK) formalism suffer from (i) limited transferability, (ii) the need for manual parametrization, and (iii) training on low-fidelity electronic structure data. To address these challenges, I introduce SlaKoNet, a parameter optimization framework that learns SK-based Hamiltonian matrix elements across 65 elements of the periodic table using automatic differentiation. SlaKoNet is trained on density functional theory data from the JARVIS-DFT database using the Tran-Blaha modified Becke-Johnson (TBmBJ) functional, encompassing over 20000 materials. The framework achieves a mean absolute error (MAE) of 0.74 eV for bandgap predictions against experimental data, representing a reasonable improvement over standard GGA functionals (MAE = 1.14 eV) while preserving the computational advantages and physical interpretability of tight-binding methods. SlaKoNet demonstrates promising scalability with up to 8.4× speedup on GPUs, enabling rapid electronic structure screening for materials discovery. SlaKoNet is publicly available at the Web site https://github.com/atomgptlab/slakonet.