Acceleration of Atomistic NEGF: Algorithms, Parallelization, and Machine Learning

• URL: http://arxiv.org/abs/2602.03438v1
• Date: Tue, 03 Feb 2026 12:01:39 GMT
• Title: Acceleration of Atomistic NEGF: Algorithms, Parallelization, and Machine Learning
• Authors: Mathieu Luisier, Nicolas Vetsch, Alexander Maeder, Vincent Maillou, Anders Winka, Leonard Deuschle, Chen Hao Xia, Manasa Kaniselvan, Marko Mladenovic, Jiang Cao, Alexandros Nikolaos Ziogas,
• Abstract summary: The Non-equilibrium Green's function (NEGF) formalism is a powerful method to simulate the quantum transport properties of nanoscale devices.<n>This paper summarizes key (algorithmic) achievements that have allowed us to bring DFT+NEGF simulations closer to the dimensions and functionality of realistic systems.
• Score: 61.12861060232382
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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