Abstract and research overview
Paper record · Original source linked above
Quantum transport calculations often require an electronic Hamiltonian as input, and constructing it from self-consistent first-principles calculations can be expensive. This conference paper combines a DeePTB-learned tight-binding Hamiltonian with the DPNEGF transport solver. It compares band structures and electronic transmission with DFT-NEGF calculations for graphene, hexagonal boron nitride and MoS2, reporting lower computational cost for these test systems. The work was presented at MoSICom 2025 and entered IEEE Xplore on 2 March 2026. The arXiv version remains available; this page does not claim a separate journal publication.
Read the IEEE conference paper. Read the arXiv version. Explore the DPNEGF code.
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Citation
Jijie Zou, Zhanghao Zhouyin, Qiangqiang Gu, and Shishir Kumar Pandey. "Deep Learning-Based Quantum Transport Simulations in Two-Dimensional Materials." 2025 International Conference on Modeling, Simulation & Intelligent Computing (MoSICom), IEEE (2025). DOI: 10.1109/MoSICom67153.2025.11398312.