用算法与机器学习加速原子尺度量子输运模拟，让真实器件仿真成为可能。
Acceleration of Atomistic NEGF: Algorithms, Parallelization, and Machine Learning
- 提出高效算法与并行化方案，提升DFT+NEGF计算效率
- 实现数千原子级器件的精确量子输运模拟
- 探索图神经网络加速第一性原理仿真，适合器件设计者
非平衡格林函数（NEGF）方法是模拟纳米器件（如晶体管、光电二极管或存储单元）量子输运特性的强大工具，适用于弹道输运或存在电子-声子、电子-光子甚至电子-电子相互作用的情形。早期仅限于包含若干原子的小系统，现已扩展至含数千原子的大型结构。模型精度也从经验型发展到全从头算（如密度泛函理论，DFT）。本文总结了关键算法进展，使DFT+NEGF模拟更接近真实器件的尺寸与功能。同时探讨了利用图神经网络与机器学习加速第一性原理器件仿真的可能性。
原文摘要 · Abstract (English)
The Non-equilibrium Green's function (NEGF) formalism is a particularly powerful method to simulate the quantum transport properties of nanoscale devices such as transistors, photo-diodes, or memory cells, in the ballistic limit of transport or in the presence of various scattering sources such as electronphonon, electron-photon, or even electron-electron interactions. The inclusion of all these mechanisms has been first demonstrated in small systems, composed of a few atoms, before being scaled up to larger structures made of thousands of atoms. Also, the accuracy of the models has kept improving, from empirical to fully ab-initio ones, e.g., density functional theory (DFT). This paper summarizes key (algorithmic) achievements that have allowed us to bring DFT+NEGF simulations closer to the dimensions and functionality of realistic systems. The possibility of leveraging graph neural networks and machine learning to speed up ab-initio device simulations is discussed as well.
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