  Since proposed in the 70s, the Non-Equilibrium Green Function (NEGF) method
has been recognized as a standard approach to quantum transport simulations.
Although it achieves superiority in simulation accuracy, the tremendous
computational cost makes it unbearable for high-throughput simulation tasks
such as sensitivity analysis, inverse design, etc. In this work, we propose
AD-NEGF, to our best knowledge the first end-to-end differentiable NEGF model
for quantum transport simulations. We implement the entire numerical process in
PyTorch, and design customized backward pass with implicit layer techniques,
which provides gradient information at an affordable cost while guaranteeing
the correctness of the forward simulation. The proposed model is validated with
applications in calculating differential physical quantities, empirical
parameter fitting, and doping optimization, which demonstrates its capacity to
accelerate the material design process by conducting gradient-based parameter
optimization.
