Learning Work Function via Implicit Reasoning on Electrostatic Potential Landscapes.

Authors

Haoyu Wan, Yue Wu, Tianhao Su, Deng Pan

Published in

Advanced science (Weinheim, Baden-Wurttemberg, Germany). Pages e77166. Aug 18, 2026. Epub Aug 18, 2026.

Abstract

Precise work function engineering in two-dimensional (2D) materials is pivotal for next-generation nanoelectronic devices. However, current data-driven approaches are often hampered by the scarcity of high-precision data and a lack of physical interpretability. We propose a Graph-Potential Cross-Modal Contrastive Learning framework designed to uncover correlations between crystal and electronic structures. Rather than performing a direct scalar mapping, our approach respects the strict thermodynamic definition of the work function (Φ  = E_vac  - E_Fermi). By extracting the vacuum level from 1D PAEP morphology and predicting the Fermi level via an auxiliary head, the model accurately predicts work functions (R² =  0.902). This indicates an automatic extraction of features governing electron escape barriers. Additionally, the model demonstrates exceptional fidelity in morphological reconstruction; predicted skewness and kurtosis of the potential surface show near-perfect linear correlation with DFT data (R² > 0.98), proving that it successfully decodes microscopic charge distribution details. This cross-modal alignment paradigm drives artificial intelligence to transcend simple numerical fitting and learn physically informative representations, facilitating future potential-contour-based inverse material design.

PMID:
42610527
Bibliographic data and abstract were imported from PubMed on 18 Aug 2026.

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