Ab initio Modeling of MoS2/Oxide Device Interfaces with Machine Learned Electronic Structures
Mirrored from arXiv — Machine Learning for archival readability. Support the source by reading on the original site.
Condensed Matter > Materials Science
Title: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.
| Subjects: | Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG) |
| Cite as: | 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
arXiv-issued DOI via DataCite
|
Submission history
From: Manasa Kaniselvan [view email][v1] Thu, 27 Aug 2026 15:33:39 UTC (7,831 KB)
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
Current browse context:
References & Citations
Bookmark
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — Machine Learning
-
Spectral-Target Physical Latent Structuring for JEPA-Style World Models
Sep 7
-
ProToMEx: Rapid, Interpretable Explanations via Structured Representations
Sep 7
-
A Data Fusion Framework for Grounding Aerospace Surrogate Model via Experimental Wind-Tunnel Observations
Sep 7
-
Quantum-Assisted Memory-Efficient Training for Parameter-Intensive Wi-Fi-Based Human Activity Recognition
Sep 7
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.