Trajectory surface hopping combined with ab initio electronic structure calculations is a popular approach for on-the-fly nonadiabatic dynamics simulations. For large systems, however, this remains a significant challenge because of the unaffordable computational cost. Here, we present an efficient divide-and-conquer approach to construct the system Hamiltonian based on Wannier analysis and machine learning. Taking advantage of the orthogonality and spatial locality of maximum localized Wannier functions, the one-electron Hamiltonian of a large system at arbitrary geometry can be directly constructed by the trained artificial neural networks. As demonstrations, we study charge transport in a zigzag graphene nanoribbon (GNR), a coved GNR, and a series of hybrid GNRs with the state-of-the-art crossing corrected global flux surface hopping method. The interplay between delocalized and localized states is found to determine the electron dynamics in hybrid GNRs. Our approach has successfully studied GNRs with over 10,000 atoms, paving the way for efficient and reliable all-atom nonadiabatic dynamics simulation of general systems.

Large-scale all-atom nonadiabatic dynamics method based on Wannier analysis and machine learning

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