Theoretical Studies on Triplet-state Driven Dissociation of Formaldehyde by Quasi-classical Molecular Dynamics Simulation on Machine-Learning Potential Energy Surface

The H-atom dissociation of formaldehyde on the lowest triplet state (T1) is studied by quasi-classical molecular dynamic simulations on the high-dimensional machine-learning potential energy surface (PES) model. Atomic-energy based deep-learning neural network (NN) is used to represent the PES function, and the weighted atom-centered symmetry functions (wACSFs) are employed as inputs of NN model to satisfy the translational, rotational, and permutational symmetries, as well as to capture the geometry feature of each atom and its individual chemical environment. Several standard technical tricks are used in the construction of NN potential energy surface (NN-PES). The accuracy of the full-dimensional NN-PES model is examined by two benchmark calculations with respect to ab initio data. Both NN and electronic-structure calculations give the similar H-atom dissociation reaction pathway on the T1 state in the intrinsic reaction coordinate (IRC) 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 T1-driven H-atom dissociation dynamics efficiently. Particularly, the dynamics simulation from different initial conditions can easily be simulated with rather low computational cost. The influence of the mode-specific vibrational excitation on the H-atom dissociation dynamics driven by the T1 state is explored. The results show that the vibrational excitation on symmetric C-H stretching, antisymmetric C-H stretching and C=O stretching motions always enhances the H-atom dissociation probability obviously.