Ab initio Modeling of MoS2/Oxide Device Interfaces with Machine Learned Electronic Structures

Abstract:We introduce a new ab initio approach to simulate semiconductor devices that integrates scalable machine-learned (ML) electronic structure models with an advanced quantum transport (QT) solver. The developed framework enables 10,000X speedups over density functional theory to produce the Hamiltonian matrix of devices made of >20,000 atoms, while offering high prediction accuracy. We use its unique features to investigate MoS2/oxide samples and single-layer MoS2 field-effect transistors, where the surrounding oxide layers, here, HfO2 or Al2O3, are explicitly included into the QT domain. In particular, we reveal that the presence of undercoordinated metal atoms (Hf or Al) close to the semiconductor-oxide interface significantly affects the magnitude of the electronic current and its propagation through MoS2.

Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)

arXiv:2608.27533 [cond-mat.mtrl-sci]

(or arXiv:2608.27533v1 [cond-mat.mtrl-sci] for this version)

https://doi.org/10.48550/arXiv.2608.27533 Focus to learn more arXiv-issued DOI via DataCite

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