Author

Yang Zhong

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Preprint Sep 2026

Linear-Scaling Quantum Transport from Machine-Learning Density Functional Theory Hamiltonians

Quantum transport simulations that combine density functional theory (DFT) with the nonequilibrium Green's function formalism (DFT-NEGF) are important to modern technology, yet their unfavorable scaling has long confined predictive simulations to small, idealized systems far below the ten-thousand-atom scale of realist...

Bang Liu, Yang Zhong, Zhi-Xin Guo et al. · 0 citations

Jul 2026

Nonadiabatic molecular dynamics on real-time excited-state surfaces via machine learning Hamiltonians

On-the-fly N${^2}$AMD (Neural network NAMD), a machine learning framework that makes on-the-fly NAMD in solids a reality, by employing an equivariant neural network to predict the system Hamiltonian, delivers excited-state energies, forces, and non-adiabatic coupling vectors at a fraction of the cost of ab initio calcu...

Changwei Zhang, Yang Zhong, Zhi-Guo Tao et al. · Physical Review Letters · 1 citation