Acceleration of Atomistic NEGF: Algorithms, Parallelization, and Machine Learning
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arXiv
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| Format: | Preprint |
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2026
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| author | Luisier, Mathieu Vetsch, Nicolas Maeder, Alexander Maillou, Vincent Winka, Anders Deuschle, Leonard Xia, Chen Hao Kaniselvan, Manasa Mladenovic, Marko Cao, Jiang Ziogas, Alexandros Nikolaos |
| author_facet | Luisier, Mathieu Vetsch, Nicolas Maeder, Alexander Maillou, Vincent Winka, Anders Deuschle, Leonard Xia, Chen Hao Kaniselvan, Manasa Mladenovic, Marko Cao, Jiang Ziogas, Alexandros Nikolaos |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_03438 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Acceleration of Atomistic NEGF: Algorithms, Parallelization, and Machine Learning Luisier, Mathieu Vetsch, Nicolas Maeder, Alexander Maillou, Vincent Winka, Anders Deuschle, Leonard Xia, Chen Hao Kaniselvan, Manasa Mladenovic, Marko Cao, Jiang Ziogas, Alexandros Nikolaos Materials Science Machine Learning 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. |
| title | Acceleration of Atomistic NEGF: Algorithms, Parallelization, and Machine Learning |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2602.03438 |