Effect of Exchange-Correlation Functionals on Schottky Barriers at Si/Metal Interfaces

Published

June 3, 2026

Author(s)

Viviana Faride Dovale Farelo, Kamal Choudhary

Abstract

Accurate prediction of Schottky barrier heights (SBHs) at metal-semiconductor (M-SC) interfaces is essential for understanding charge injection in electronic and optoelectronic devices. However, conventional density functional theory (DFT) methods struggle to simultaneously describe the semiconductor bandgap and the metal Fermi level, limiting their predictive power. Here, we introduce \textbfSchottkyMat}, an automated and scalable workflow that combines DFT and machine learning to compute SBHs across diverse M-SC systems. The framework builds upon the JARVIS-DFT database and incorporates interface generation via the Zur algorithm in InterMat, along with ALIGNN-FF force field pre-relaxation, enabling high-throughput exploration of realistic interface models. We apply this workflow to silicon-metal interfaces, including Al, Cu, Ag, and Au. We evaluate several exchange-correlation (XC) functionals and propose a modified mBJ-based approach (\textbfSB-mBJ}) that separately tunes the metal and semiconductor electronic structure. Both SB-mBJ and hybrid HSE06 yield excellent agreement with experimental SBHs, with SB-mBJ offering greater flexibility for systems involving heavy metals such as Ag and Au. The results underscore the importance of improving semiconductor electronic structure accuracy and show that SchottkyMat provides a transferable and efficient solution for interface property prediction across materials classes.

Citation

Journal of Physical Chemistry C

Volume

130

Issue

24

Pub Type

Journals

Download Paper

https://doi.org/10.1021/acs.jpcc.6c01668

Local Download

Keywords

Schottky barrier, density functional theory

Citation

Dovale Farelo, V. and Choudhary, K. (2026), Effect of Exchange-Correlation Functionals on Schottky Barriers at Si/Metal Interfaces, Journal of Physical Chemistry C, [online], https://doi.org/10.1021/acs.jpcc.6c01668, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=960385 (Accessed June 26, 2026)

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