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Slakonet: A Unified Slater-koster Tight-binding Framework Using Neural Network Infrastructure For The Periodic Table.

Home
Slakonet: A Unified Slater-koster Tight-binding Framework Using Neural Network Infrastructure For The Periodic Table.

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

Kamal Choudhary^1,2

¹Department of Materials Science and Engineering, Whiting School of Engineering, The Johns Hopkins University, Baltimore, Maryland 21218, United States.

The Journal of Physical Chemistry Letters

|October 17, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

SlaKoNet efficiently predicts electronic band structures using a learned Slater-Koster formalism. This framework improves accuracy over traditional methods and enables rapid materials discovery.

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Area of Science:

Computational Materials Science
Quantum Chemistry
Solid-State Physics

Background:

Accurate electronic band structure prediction is crucial for materials design.
Existing machine learning models lack universality, and traditional Slater-Koster (SK) models have limited transferability and require manual parameterization.
Training on low-fidelity data hinders traditional SK model performance.

Purpose of the Study:

To develop a universal and efficient framework for predicting electronic band structures.
To overcome the limitations of existing machine learning and traditional tight-binding models.
To enable rapid electronic structure screening for novel materials discovery.

Main Methods:

Introduced SlaKoNet, a parameter optimization framework learning SK-based Hamiltonian matrix elements.

Utilized automatic differentiation for parameter learning across 65 elements.

Trained on over 20,000 materials from the JARVIS-DFT database using the TBmBJ functional.

Main Results:

Achieved a mean absolute error (MAE) of 0.74 eV for bandgap predictions against experimental data.
Demonstrated improvement over standard GGA functionals (MAE = 1.14 eV).
Showcased scalability with up to 8.4x speedup on GPUs.

Conclusions:

SlaKoNet offers a computationally advantageous and interpretable alternative to traditional methods.
The framework enables efficient and accurate electronic structure screening.
SlaKoNet facilitates accelerated materials discovery with targeted properties.