Deep learning-accelerated NEGF formalism for autonomous design of quantum transport in microscopic heterostructures.
Two-dimensional (2D) materials exhibit a wide range of electronic properties that make them promising candidates for next-generation nanoelectronic devices. Accurate prediction of their quantum transport behavior is therefore of both fundamental and technological importance. While the Non-Equilibrium Green's Function (NEGF) formalism coupled with Density Functional Theory (DFT) provides reliable insights, its high computational cost limits applications to large-scale or high-throughput studies. Here we present DeePTB-NEGF, a framework that combines a deep learning-based tight-binding Hamiltonian derived directly from first-principles calculations (DeePTB) with efficient quantum transport simulations implemented in the DPNEGF package. We validate the method on five prototypical 2D materials (graphene, hexagonal boron nitride (h-BN), [Formula: see text], [Formula: see text], and black phosphorus) demonstrating excellent agreement with conventional DFT-NEGF for band structures and transmission spectra. Beyond single-material benchmarks, we showcase the framework's versatility by exploring strain engineering (uniaxial strain on graphene and biaxial strain on [Formula: see text]), substitution doping in [Formula: see text], and current-voltage characteristics of a graphene field-effect transistor (FET). A scaling analysis reveals that DeePTB-NEGF can simulate systems with hundreds of atoms in minutes, achieving speed-ups of over [Formula: see text] compared to DFT-NEGF for heterostructures such as graphene/h-BN/graphene. These results establish DeePTB-NEGF as a powerful tool for autonomous, high-throughput design of quantum transport in microscopic heterostructures, enabling rapid prototyping of next-generation 2D devices.