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.
甲醛在最低三重态 (
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) 通过对高维机器学习势能面 (PES) 模型的准经典分子动力学模拟进行研究。使用基于原子能的深度学习神经网络 (NN) 来表示 PES 函数，并采用加权的原子中心对称函数作为神经网络模型的输入，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的构建中使用了几个标准的技术技巧，包括聚类算法在训练数据集的形成中的应用，通过不同拟合的NN模型检验NN-PES模型的可靠性，以及检测通过训练数据集的置信区间确定失信区域。
从头
数据。在本征反应坐标分析中，NN 和电子结构计算都给出了
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态上类似的 H 原子解离反应途径。基于 NN-PES 和
ab initio
PES的小规模试验动力学模拟给出了高度一致的结果。确认NN-PES的准确性后，在准经典动力学中计算了大量的轨迹，这让我们更好地理解了
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有效地驱动 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。探索了模式特定的振动激发对由
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状态驱动的 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显着提高 H 原子的解离概率。
The H-atom dissociation of formaldehyde on the lowest triplet state (
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) 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
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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
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-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
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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.
