Deep learning (DL) is applied to simulate non-adiabatic molecular dynamics of phenanthrene, using the time-dependent density functional based tight binding (TD-DFTB) approach for excited states combined with mixed quantum–classical propagation. Reference calculations rely on Tully’s fewest-switches surface hopping (FSSH) algorithm coupled to TD-DFTB, which provides electronic relaxation dynamics in fair agreement with various available experimental results. Aiming at describing the coupled electron-nuclei dynamics in large molecular systems, we then examine the combination of DL for excited-state potential energy surfaces (PESs) with a simplified trajectory surface hopping propagation based on the Belyaev–Lebedev (BL) scheme. We start to assess the accuracy of the TD-DFTB approach upon comparison of the optical spectrum with experimental and higher-level theoretical results. Using the recently developed SchNetPack (Schtt et al 2019 J. Chem. Theory Comput. 15 448–55) for DL applications, we train several models and evaluate their performance in predicting excited-state energies and forces. Then, the main focus is given to the analysis of the electronic population of low-lying excited states computed with the aforementioned methods. We determine the relaxation timescales and compare them with experimental data. Our results show that DL demonstrates its ability to describe the excited-state PESs. When coupled to the simplified BL scheme considered in this study, it provides reliable description of the electronic relaxation in phenanthrene as compared with either the experimental data or the higher-level FSSH/TD-DFTB theoretical results. Furthermore, the DL performance allows high-throughput analysis at a negligible cost.

深度学习 (DL) 用于模拟菲的非绝热分子动力学，使用基于时间依赖的密度泛函的紧束缚 (TD-DFTB) 方法结合混合量子经典传播的激发态。参考计算依赖于与 TD-DFTB 耦合的 Tully 最少开关表面跳跃 (FSSH) 算法，该算法提供与各种可用实验结果相当一致的电子弛豫动力学。为了描述大分子系统中的耦合电子-核动力学，我们然后检查了激发态势能面（PES）的 DL 与基于 Belyaev-Lebedev（BL）方案的简化轨迹表面跳跃传播的组合。通过将光谱与实验和更高级别的理论结果进行比较，我们开始评估 TD-DFTB 方法的准确性。使用最近开发的 SchNetPack (Schtt等人2019 年化学杂志。理论计算。 15448-55) 对于 DL 应用，我们训练了几个模型并评估它们在预测激发态能量和力方面的性能。然后，主要关注使用上述方法计算的低激发态电子布居的分析。我们确定松弛时间尺度并将它们与实验数据进行比较。我们的结果表明，DL 展示了其描述激发态 PES 的能力。当与本研究中考虑的简化 BL 方案结合时，与实验数据或更高级别的 FSSH/TD-DFTB 理论结果相比，它提供了菲中电子弛豫的可靠描述。此外，DL 性能允许以可忽略不计的成本进行高通量分析。