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The supervised machine learning (ML) approach is applied to realize thetrajectory-based nonadiabatic dynamics within the framework of the symmetricalquasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian(MM-SQC). After the construction of the long short-term memory recurrent neuralnetwork (LSTM-RNN) model, it is used to perform the entire trajectoryevolutions from initial sampling conditions. The proposed idea is proven to bereliable and accurate in the simulations of the dynamics of severalsite-exciton electron-phonon coupling models, which cover two-site andthree-site systems with biased and unbiased energy levels, as well as include afew or many phonon modes. The LSTM-RNN approach also shows the powerful abilityto obtain the accurate and stable results for the long-time evolutions. Itindicates that the LSTM-RNN model perfectly captures of dynamical correctioninformation in the trajectory evolution in the MM-SQC dynamics. Our workprovides the possibility to employ the ML methods in the simulation of thetrajectory-based nonadiabatic dynamic of complex systems with a large number ofdegrees of freedoms.

Realization of the Trajectory Propagation in the MM-SQC Dynamics by Using Machine Learning