论文标题
通过使用机器学习，在MM-SQC动力学中实现轨迹传播
Realization of the Trajectory Propagation in the MM-SQC Dynamics by Using Machine Learning
论文作者
论文摘要
采用监督的机器学习（ML）方法来实现基于Meyer-Miller映射汉密尔顿（MM-SQC）的对称准准经典动力学框架内的基于轨迹的非绝热动态。在构建长期短期记忆复发性神经网络（LSTM-RNN）模型之后，它用于从初始采样条件下执行整个轨迹演变。事实证明，该想法在模拟了几种站点效率电子偶联模型的动力学中是可靠和准确的，该模型涵盖了具有偏见和无偏见的能量水平的二站点和三个位点系统，还包括几种或几个声子模式。 LSTM-RNN方法还显示了能够获得长期发展的准确稳定结果的强大能力。它表明LSTM-RNN模型完美地捕获了MM-SQC动力学中轨迹演化中动态校正信息的信息。我们的工作提供了在模拟基于轨迹的非绝热动态的复杂系统中具有大量自由度的轨迹的非绝热动态的可能性。
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.
