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# A Vote for New Projects: DPNEGF ## Proposal DPNEGF is a Python package that integrates the Deep Learning Tight-Binding (DeePTB) approach with the Non-Equilibrium Green’s Function (NEGF) method, establishing an efficient quantum transport simulation framework DeePTB-NEGF with first-principles accuracy. By using DeePTB-SK or DeePTB-E3—both available within the DeePTB package—DeePTB-NEGF can compute quantum transport properties in open-boundary systems with either environment-corrected Slater-Koster TB Hamiltonian or linear combination of atomic orbitals (LCAO) Kohn-Sham Hamiltonian. For more information, see the paper [DeePTB-NEGF: arXiv:2411.08800v2](https://arxiv.org/abs/2411.08800v2) ## Deadline The vote will be open for at least 6 days unless there is an objection. ## Scope TOC MEMBERS.
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A Vote for New Projects: DPNEGF
Proposal
DPNEGF is a Python package that integrates the Deep Learning Tight-Binding (DeePTB) approach with the Non-Equilibrium Green’s Function (NEGF) method, establishing an efficient quantum transport simulation framework DeePTB-NEGF with first-principles accuracy.
By using DeePTB-SK or DeePTB-E3—both available within the DeePTB package—DeePTB-NEGF can compute quantum transport properties in open-boundary systems with either environment-corrected Slater-Koster TB Hamiltonian or linear combination of atomic orbitals (LCAO) Kohn-Sham Hamiltonian.
For more information, see the paper DeePTB-NEGF: arXiv:2411.08800v2
Deadline
The vote will be open for at least 6 days unless there is an objection.
Scope
TOC MEMBERS.