Quantum transport calculations are essential for understanding and designing nanoelectronic devices, yet the trade-off between accuracy and computational efficiency has long limited their practical applications. We present a general framework that combines the deep learning tight-binding Hamiltonian (DeePTB) approach with the non-equilibrium Green's Function (NEGF) method, enabling efficient quantum transport calculations while maintaining first-principles accuracy. We demonstrate the capabilities of the DeePTB-NEGF framework through two representative applications: comprehensive simulation of break junction systems, where conductance histograms show good agreement with experimental measurements in both metallic contact and single-molecule junction cases; and simulation of carbon nanotube field effect transistors through self-consistent NEGF-Poisson calculations, capturing essential physics including the electrostatic potential and transfer characteristic curves under finite bias conditions. This framework bridges the gap between first-principles accuracy and computational efficiency, providing a powerful tool for high-throughput quantum transport simulations across different scales in nanoelectronics.

翻译：量子输运计算对于理解和设计纳米电子器件至关重要，然而精度与计算效率之间的权衡长期以来限制了其实际应用。我们提出了一种通用框架，将深度学习紧束缚哈密顿量（DeePTB）方法与非平衡格林函数（NEGF）方法相结合，在保持第一性原理精度的同时，实现了高效的量子输运计算。我们通过两个代表性应用展示了DeePTB-NEGF框架的能力：断裂结系统的综合模拟，其电导直方图在金属接触和单分子结情况下均与实验测量结果良好吻合；以及通过自洽的NEGF-Poisson计算模拟碳纳米管场效应晶体管，捕捉了包括有限偏压条件下的静电势和转移特性曲线在内的关键物理特性。该框架弥合了第一性原理精度与计算效率之间的差距，为纳米电子学中跨尺度的高通量量子输运模拟提供了一个强大工具。