Physics-Informed Machine Learning for Predicting p-Type Schottky Barriers

doi:10.1016/j.surfin.2025.108275

Physics-Informed Machine Learning for Predicting p-Type Schottky Barriers

2025-12-06

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DOI：10.1016/j.surfin.2025.108275

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P-type Schottky barrier height (p-SBH) is critical for high-performance 2D electronics, but its modulation is particularly challenging due to complicated interfacial contact associated with lattice mismatch and electronic interactions. In this report, we reported a robust model, namely XGBoost, to predict p-SBHs after an investigation of 60 graphene/transition metal dichalcogenide MX2 (TMDC, M=Mo, W; X = S, Se, Te) heterointerfaces with a combined density functional theory (DFT) and machine learning. Specifically, XGBoost achieves an accuracy R2 >0.95 and MAE <0.06 eV primarily due to the use of key descriptors (interlayer spacing, tunneling probability and chalcogen properties). Such physics-informed approach advances the knowledge of heterojunction structures and enhances the predictive capacity, providing insights for rational design of functional nanoelectronic interfaces.

Physics-Informed Machine Learning for Predicting p-Type Schottky Barriers

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