Institute of Atomic and Molecular Physics, Sichuan University, Chengdu 610065, China.
College of Physics, Sichuan University, Chengdu 610065, China.
J Chem Phys. 2023 Jun 28;158(24). doi: 10.1063/5.0151331.
This work studies the exhaustive rovibrational state-specific collision-induced dissociation properties of the N2+N system by QCT (quasi-classical trajectory) combined with a neural network method based on the ab initio PES recently published by Varga et al. [Phys. Chem. Chem. Phys. 23, 26273 (2021)]. The QCT combined with a neural network for state-specific dissociation (QCT-NN-SSD) model is developed and used to predict the dissociation cross sections and their energy dependence on the thermal range from a sparsely sampled noisy dataset. It is conservatively estimated that using this method can reduce the cost of the calculation by 96.5%. The rate coefficient of thermal non-equilibrium between different energy modes is obtained by combining the QCT-NN-SSD model with the multi-temperature model. The results show that, for the equilibrium state, dissociation mainly occurs at high vibrational and moderately low rotational levels. When the system is in non-equilibrium, there is no obvious vibrational level preference and highly rotationally excited molecules play a major role in promoting the dissociation by compensating for the lack of vibrational energy. The use of neural network training to generate complete datasets based on limited and discrete data provides an economical and reliable way to obtain a complete kinetic database needed to accurately simulate non-equilibrium flows.
这项工作通过 QCT（准经典轨迹）结合基于 Varga 等人最近发表的从头算 PES 的神经网络方法研究了 N2+N 系统的详尽的振转态特异性碰撞诱导解离特性。[Phys. Chem. Chem. Phys. 23, 26273 (2021)]。开发了用于状态特异性解离的 QCT 与神经网络结合模型（QCT-NN-SSD），并用于从稀疏采样的噪声数据集预测在热范围内的离解截面及其能量依赖性。保守估计，使用这种方法可以将计算成本降低 96.5%。通过将 QCT-NN-SSD 模型与多温度模型相结合，获得了不同能量模式之间的热非平衡速率系数。结果表明，对于平衡状态，离解主要发生在高振动和中等低转动能级。当系统处于非平衡状态时，没有明显的振动能级偏好，高度转动激发的分子通过补偿振动能的缺乏来促进离解，从而发挥主要作用。使用神经网络训练基于有限和离散数据生成完整数据集，为基于准确模拟非平衡流所需的完整动力学数据库提供了一种经济可靠的方法。
