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.

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BibTeX RIS

BibTeX

@misc{kaniselvan2026initio,
  title = {{Ab initio Modeling of MoS2/Oxide Device Interfaces with Machine Learned Electronic Structures}},
  author = {Manasa Kaniselvan and Mauro Dossena and Denghui Lu and Alexander Maeder and Nicolas Vetsch and Alexandros Nikolaos Ziogas and Mathieu Luisier},
  howpublished = {arXiv},
  year = {2026},
  month = aug,
  doi = {10.48550/arxiv.2608.27533},
  eprint = {2608.27533},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2608.27533},
}

RIS

TY  - UNPB
TI  - Ab initio Modeling of MoS2/Oxide Device Interfaces with Machine Learned Electronic Structures
AU  - Manasa Kaniselvan
AU  - Mauro Dossena
AU  - Denghui Lu
AU  - Alexander Maeder
AU  - Nicolas Vetsch
AU  - Alexandros Nikolaos Ziogas
AU  - Mathieu Luisier
PY  - 2026
DA  - 2026/08/27
PB  - arXiv
DO  - 10.48550/arxiv.2608.27533
UR  - https://arxiv.org/abs/2608.27533
AB  - 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.
ER  -

Abstract

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