SlakoNet

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 parameterization, 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), 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.

Key Features

• Universal parameterization: Works across 65 elements and their combinations
• Physics-informed: Based on Slater-Koster tight-binding formalism
• High accuracy: Mean absolute error of 0.74 eV for band gaps vs experimental values
• Scalable: GPU-accelerated calculations for systems up to 2000 atoms
• Comprehensive properties: Predicts band structures, DOS, band gaps, and orbital projections

Installation

pip install slakonet

or

git clone https://github.com/atomgptlab/slakonet.git
cd slakonet
pip install -e .

Quick Start

Google Colab example

Open in Colab

Example of Training Models

python slakonet/train_slakonet.py --config_name slakonet/examples/config_example.json

Example of Inference

python slakonet/predict_slakonet.py  --file_path slakonet/examples/POSCAR-JVASP-107.vasp

Using Pretrained Models in Python

from slakonet.optim import (
    MultiElementSkfParameterOptimizer,
    get_atoms,
    kpts_to_klines,
    default_model,
)
import torch
from slakonet.atoms import Geometry
from slakonet.main import generate_shell_dict_upto_Z65

model = default_model()

# Get structure (example with JARVIS ID)
atoms, opt_gap, mbj_gap = get_atoms("JVASP-107")  
geometry = Geometry.from_ase_atoms([atoms.ase_converter()])
shell_dict = generate_shell_dict_upto_Z65()

# Compute electronic properties
with torch.no_grad():
    properties, success = model.compute_multi_element_properties(
        geometry=geometry,
        shell_dict=shell_dict,
        get_fermi=True,
        device="cuda"
    )

# Access results
print(f"Band gap: {properties['band_gap_eV']:.3f} eV")
print(f"Fermi energy: {properties['fermi_energy_eV']:.3f} eV")

# Plot band structure and DOS
eigenvalues = properties["eigenvalues"]
dos_values = properties['dos_values_tensor']
dos_energies = properties['dos_energy_grid_tensor']

Supported Materials

• Elements: Z = 1-65
• Material classes: Oxides, carbides, nitrides, chalcogenides, halides, intermetallics
• Crystal structures: All major structure types

Performance Benchmarks

• Accuracy: 0.76 eV MAE for band gaps (vs 0.38 eV for reference TB-mBJ DFT)
• Speed: <10 seconds for 1000-atom systems on GPU
• Scalability: Efficient with GPU acceleration
• Coverage: Validated on 50 semiconductor/insulator compounds for experiments

Output Properties

SlakoNet predicts comprehensive electronic properties including:

• Electronic band structures along high-symmetry k-paths
• Total and projected density of states (DOS)
• Band gaps (direct/indirect) and band edges
• Fermi energy and electronic structure topology
• Atom-projected and orbital-projected DOS (s/p/d contributions)

Applications

• High-throughput materials screening
• Electronic structure prediction without expensive DFT
• Band structure and DOS calculations for device design
• Semiconductor and quantum materials discovery
• Educational tools for solid-state physics

Dataset

• Figshare TBmBJ dataset

Methodology

SlakoNet employs a neural network to learn distance-dependent Slater-Koster parameters:

• Basis set: sp³d tight-binding orbitals
• Training data: JARVIS-DFT with TB-mBJ functional
• Loss function: Combined DOS + band gap optimization
• Framework: PyTorch with GPU acceleration
• Cutoff radius: 7 Å for orbital interactions

Limitations

• Limited to elements Z ≤ 65
• Trained on specific meta-GGA DFT (TBmBJ)
• Discrepancies in conduction band descriptions
• No self-consistent cycle
• No spin-orbit coupling or magnetic properties

Citation

If you use SlakoNet in your research, please cite:

@article{choudhary2025slakonet,
  title={SlaKoNet: A Unified Slater-Koster Tight-Binding Framework Using Neural Network Infrastructure for the Periodic Table},
  author={Choudhary, Kamal},
  journal={ChemRxiv},
  doi={https://doi.org/10.26434/chemrxiv-2025-4vjr9-v2},
  year={2025}
}