原子istic NEGF加速：算法、并行化与机器学习
Acceleration of Atomistic NEGF: Algorithms, Parallelization, and Machine Learning
AI总结：
本文提出利用算法、并行化和机器学习加速DFT+NEGF模拟，以更精确地模拟现实系统的量子输运特性。
摘要：
非平衡格林函数（NEGF）方法是一种特别强大的方法，用于模拟纳米尺度设备（如晶体管、光二极管或存储单元）的量子输运特性，在输运的玻色极限或存在各种散射源（如电子-声子、电子-光子或甚至电子-电子相互作用）的情况下。将所有这些机制纳入其中最初是在由几个原子组成的微小系统中实现的，随后扩展到由数千个原子组成的更大结构。此外，模型的准确性也一直在提高，从经验性到完全的从头计算方法，例如密度泛函理论（DFT）。本文总结了关键（算法）成就，使DFT+NEGF模拟能够更接近现实系统的尺寸和功能。此外，讨论了利用图神经网络和机器学习加速从头计算设备模拟的可能性。
英文摘要：
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.
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。
