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

• URL: http://arxiv.org/abs/2207.05556v1
• Date: Mon, 11 Jul 2022 01:23:36 GMT
• Title: Realization of the Trajectory Propagation in the MM-SQC Dynamics by Using Machine Learning
• Authors: Kunni Lin, Jiawei Peng, Chao Xu, Feng Long Gu and Zhenggang Lan
• Abstract summary: We apply the supervised machine learning (ML) approach to realize the trajectory-based nonadiabatic dynamics. The proposed idea is proven to be reliable and accurate in the simulations of the dynamics of several site-exciton electron-phonon coupling models.
• Score: 4.629634111796585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The supervised machine learning (ML) approach is applied to realize the trajectory-based nonadiabatic dynamics within the framework of the symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian (MM-SQC). After the construction of the long short-term memory recurrent neural network (LSTM-RNN) model, it is used to perform the entire trajectory evolutions from initial sampling conditions. The proposed idea is proven to be reliable and accurate in the simulations of the dynamics of several site-exciton electron-phonon coupling models, which cover two-site and three-site systems with biased and unbiased energy levels, as well as include a few or many phonon modes. The LSTM-RNN approach also shows the powerful ability to obtain the accurate and stable results for the long-time evolutions. It indicates that the LSTM-RNN model perfectly captures of dynamical correction information in the trajectory evolution in the MM-SQC dynamics. Our work provides the possibility to employ the ML methods in the simulation of the trajectory-based nonadiabatic dynamic of complex systems with a large number of degrees of freedoms.

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