Lin Shichen, Peng Daoling, Yang Weitao, Gu Feng Long, Lan Zhenggang
Key Laboratory of Theoretical Chemistry of Environment, Ministry of Education, South China Normal University, Guangzhou 510006, People's Republic of China.
Department of Chemistry, Duke University, Durham, North Carolina 27708, USA.
J Chem Phys. 2021 Dec 7;155(21):214105. doi: 10.1063/5.0067176.
The H-atom dissociation of formaldehyde on the lowest triplet state (T) is studied by quasi-classical molecular dynamic simulations on the high-dimensional machine-learning potential energy surface (PES) model. An atomic-energy based deep-learning neural network (NN) is used to represent the PES function, and the weighted atom-centered symmetry functions are employed as inputs of the NN model to satisfy the translational, rotational, and permutational symmetries, and to capture the geometry features of each atom and its individual chemical environment. Several standard technical tricks are used in the construction of NN-PES, which includes the application of clustering algorithm in the formation of the training dataset, the examination of the reliability of the NN-PES model by different fitted NN models, and the detection of the out-of-confidence region by the confidence interval of the training dataset. The accuracy of the full-dimensional NN-PES model is examined by two benchmark calculations with respect to ab initio data. Both the NN and electronic-structure calculations give a similar H-atom dissociation reaction pathway on the T state in the intrinsic reaction coordinate analysis. The small-scaled trial dynamics simulations based on NN-PES and ab initio PES give highly consistent results. After confirming the accuracy of the NN-PES, a large number of trajectories are calculated in the quasi-classical dynamics, which allows us to get a better understanding of the T-driven H-atom dissociation dynamics efficiently. Particularly, the dynamics simulations from different initial conditions can be easily simulated with a rather low computational cost. The influence of the mode-specific vibrational excitations on the H-atom dissociation dynamics driven by the T state is explored. The results show that the vibrational excitations on symmetric C-H stretching, asymmetric C-H stretching, and C=O stretching motions always enhance the H-atom dissociation probability obviously.
通过在高维机器学习势能面（PES）模型上进行准经典分子动力学模拟，研究了甲醛在最低三重态（T）上的H原子解离。基于原子能量的深度学习神经网络（NN）用于表示PES函数，加权原子中心对称函数用作NN模型的输入，以满足平移、旋转和置换对称性，并捕捉每个原子的几何特征及其各自的化学环境。在NN-PES的构建中使用了几种标准技术技巧，包括在训练数据集形成中应用聚类算法、通过不同拟合的NN模型检查NN-PES模型的可靠性以及通过训练数据集的置信区间检测置信区间外的区域。通过相对于从头算数据的两个基准计算来检验全维NN-PES模型的准确性。在本征反应坐标分析中，NN计算和电子结构计算在T态上给出了相似的H原子解离反应途径。基于NN-PES和从头算PES的小规模试验动力学模拟给出了高度一致的结果。在确认NN-PES的准确性之后，在准经典动力学中计算了大量轨迹，这使我们能够有效地更好地理解T驱动的H原子解离动力学。特别是，可以以相当低的计算成本轻松模拟来自不同初始条件的动力学模拟。探索了特定模式振动激发对由T态驱动的H原子解离动力学的影响。结果表明，对称C-H伸缩、不对称C-H伸缩和C=O伸缩运动的振动激发总是明显提高H原子解离概率。
