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The dynamics and kinetics of spin-forbidden transitions between N(2D) and N(4S) via collisions with N2 molecules are investigated using a quantum wave packet (WP) method and the semi-classical coherent switches...

In low temperature plasmas, energy transfer between asymmetric stretching excited CO2 molecules can be highly efficient, which leads to further excitation (and de-excitation) of the CO2 molecules: CO2(vas) + CO2(vas) → CO2(vas + 1) + CO2(vas − 1). Through such a vibrational ladder climbing mechanism, CO2 can be activated and eventually dissociates. To gain mechanistic insight of such processes, a full-dimensional accurate potential energy surface (PES) for the CO2 + CO2 system is developed using the permutational invariant polynomial-neural network method based on CCSD(T)-F12a/AVTZ energies at about 39 000 geometries. This PES is used in quasi-classical trajectory (QCT) studies of the vibrational energy transfer between CO2 molecules excited in the asymmetric stretching mode. A machine learning algorithm is used to determine state-specific rate coefficients for the vibrational transfer processes from a limited data set. In addition to the CO2(vas + 1) + CO2(vas − 1) channel, the QCT simulations revealed significant contributions from the CO2(vas + 2,3) + CO2(vas − 2,3) channels, particularly at low collision energies/temperatures. These multi-vibrational-quantum processes are attributed to enhanced energy flow in the collisional complex formed by enhanced dipole–dipole interaction between asymmetric stretching excited CO2 molecules.

For the symmetric reaction OH + H2O → H2O + OH, ∼48 000 data points are sampled and calculated at the level of the explicitly correlated unrestricted coupled cluster method with single, double, and perturbative triple excitations with the augmented correlation-consistent polarized triple zeta basis set (CCSD(T)-F12a/AVTZ). The data set is then employed to fit the full dimensional accurate potential energy surface (PES) by using the permutation invariant polynomial-neural network (PIP-NN) method, resulting in a total root mean square error (RMSE) of 0.12 kcal mol−1. The quasi-classical trajectory (QCT) method is used to study its reaction dynamics. It has been found that the integral cross section (ICS) is increased gradually as a function of the collision energy ranging from 8 to 30 kcal mol−1 with a threshold around 8 kcal mol−1. At a collision energy of 20 kcal mol−1, detailed dynamics show that the OH product is a spectator and the differential cross section (DCS) is dominated by backward scattering with considerable contributions from sideway scattering, consistent with the direct rebound and stripping mechanisms.

The reaction kinetics of the heavy-light-heavy abstraction reaction F + HCl → HF + Cl on the ground electronic state potential energy surface (PES) is investigated theoretically by a recently developed ring polymer molecular dynamics (RPMD) approach. First, a new PES is developed by the permutation invariant polynomial neural network (PIP-NN) approach based on 30 620 points sampled over a large configuration space from the latest and most accurate Deskevich−Hayes−Takahashi−Skodje−Nesbitt (DHTSN) PES (J. Chem. Phys., 2006, 124, 224303). Excellent fitting performance was achieved with only 521 parameters. The PIP-NN PES is 11 times faster than the DHTSN PES. Besides, the first analytical derivatives with respect to the coordinates of the atoms have been obtained for the PIP-NN PES. The RPMD rate coefficients on the PIP-NN PES are calculated and compared with available theoretical and experimental results. It is found that the experimental rate coefficients are significantly larger than the theoretical results on the DHTSN PES, due to its overestimated reaction barrier. We conclude that a reliable PES for this important heavy-light-heavy reaction is highly desirable.

Full-dimensional coupled-channel quantum scattering and quasi-classical trajectory calculations have been carried out and analyzed to unravel the mode specific dynamics in the H2 + SH → H + H2S reaction employing two different accurate neural network representations of an ab initio global potential energy surface at the coupled cluster level. Strong mode selectivity was found and partially rationalized by the sudden vector projection model. Specifically, the vibrational excitation of H2 remarkably enhances the reactivity while its rotational excitation only slightly promotes the reaction. On the other hand, the reactant SH acts as a good spectator, whose vibrational and rotational excitations have a negligible effect on the reaction.