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

arXiv: https://arxiv.org/abs/2602.03438 · HTML: https://mlsanity.com/paper/2602.03438/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Builds on (most cited first, 2 of 2)

- [A Data-Centric Approach to Extreme-Scale Ab initio Dissipative Quantum Transport Simulations](https://mlsanity.com/paper/1912.10024/) (2019)
- [Serinv: A Scalable Library for the Selected Inversion of Block-Tridiagonal with Arrowhead Matrices](https://mlsanity.com/paper/2503.17528/) (2025)

## Abstract (arXiv)

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.