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