D. Malkoç

Physics-Informed Feature Engineering for Machine Learning Prediction of Tight Binding Parameters in III-V Semiconductors

This work predicts sp tight binding parameters for nine III-V binary compounds from experimentally accessible observables without first-principles input, indicating that physics-informed feature engineering supports accurate, interpretable parameter prediction in the small-data regime.