arXiv (OAI Expanded)
open access
Ab initio Modeling of MoS2/Oxide Device Interfaces with Machine Learned Electronic Structures
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
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Ab initio Modeling of MoS2/Oxide Device Interfaces with Machine Learned Electronic Structures#780843
- Complete Neural Electronic Initialization Accelerates Materials DFT#1036149
- Complete Neural Electronic Initialization Accelerates Materials DFT#1006867
- Learning new physics from data: A symmetrized approach#676001
- Gate-Reconfigurable Single- and Double-Dot Transport in Trilayer MoSe2#1105009
Record · ID 779468
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive