Deep learning accelerated quantum transport simulations in nanoelectronics: from break junctions to field-effect transistors

Quantum transport simulations are essential for understanding and designing nanoelectronic devices, yet the long-standing trade-off between accuracy and computational efficiency has limited their practical applications. We present DeePTB-NEGF, an integrated framework combining deep learning tight-binding Hamiltonian prediction with non-equilibrium Green’s function methodology to enable accurate quantum transport simulations in open boundary conditions with 2–3 orders of magnitude acceleration. We demonstrate DeePTB-NEGF through two challenging applications: comprehensive break junction simulations with over 104 snapshots, showing excellent agreement with experimental conductance histograms; and carbon nanotube field-effect transistors (CNT-FETs) at experimental dimensions, reproducing measured transfer characteristics for a 41 nm channel CNT-FET (~8000 atoms, 3 × 104 orbitals) and predicting zero-bias transmission spectra for a 180 nm CNT (~3 × 104 atoms, 105 orbitals), showcasing the framework’s capability for large-scale device simulations. Our systematic studies across varying geometries confirm the necessity of simulating realistic experimental structures for precise predictions. DeePTB-NEGF bridges the longstanding gap between first-principles accuracy and computational efficiency, providing a scalable tool for high-throughput and large-scale quantum transport simulations that enable previously inaccessible nanoscale device investigations.