论文标题
AD-NEGF：用于灵敏度分析和反问题的端到端可区分量子传输模拟器
AD-NEGF: An End-to-End Differentiable Quantum Transport Simulator for Sensitivity Analysis and Inverse Problems
论文作者
论文摘要
自从在70年代提出以来，非平衡绿色函数（NEGF）方法已被认为是量子传输模拟的标准方法。尽管它在模拟精度方面取得了优势，但巨大的计算成本使其无法忍受高通量仿真任务，例如灵敏度分析，逆设计等。在这项工作中，我们提出了AD-NEGF，因此，我们的最佳知识是第一个用于量子传输仿真的端到端的端到端区分的NEGF模型。我们在pytorch中实现了整个数值过程，并使用隐式层技术定制的向后通过，该技术以负担得起的成本提供梯度信息，同时保证正向模拟的正确性。提出的模型通过应用在计算差异物理量，经验参数拟合和掺杂优化方面进行了验证，该模型通过进行基于梯度的参数优化来证明其能够加速材料设计过程的能力。
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.
