AD-NEGF：用于灵敏度分析与反问题的端到端可微分量子输运模拟器
AD-NEGF: An End-to-End Differentiable Quantum Transport Simulator for Sensitivity Analysis and Inverse Problems
AI总结：
本文提出首个端到端可微分NEGF量子输运模拟器AD-NEGF，在PyTorch中结合隐式层技术实现高效梯度计算，用于灵敏度分析、参数拟合与掺杂优化，加速材料设计。
摘要：
自20世纪70年代提出以来，非平衡格林函数（NEGF）方法已被公认为量子输运模拟的标准方法。尽管它在模拟精度上具有优势，但其巨大的计算成本使其难以承受高通量模拟任务，如灵敏度分析、反设计等。在这项工作中，我们提出了AD-NEGF，据我们所知这是第一个用于量子输运模拟的端到端可微分NEGF模型。我们在PyTorch中实现了整个数值过程，并利用隐式层技术设计了定制化的反向传播过程，在保证前向模拟正确性的同时，以可承受的成本提供梯度信息。所提出的模型通过计算微分物理量、经验参数拟合和掺杂优化等应用进行了验证，证明了其通过基于梯度的参数优化来加速材料设计过程的能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
