Supercollisions of fast H-atom with ethylene on an accurate full-dimensional potential energy surface

High-temperature non-equilibrium atom-diatom collisional energy transfer

The change of the vibrational energy within a molecule after collisions with another molecule plays an essential role in the evolution of molecular internal energy distributions, which is also the limiting process in the relaxation of gases toward equilibrium. Here, we investigate the energy transfer between the translational motion and the vibrational motion of the diatom during the atom-diatom collision, the simplest case involving the transfer between inter-molecular and intra-molecular energies. We are interested in the situation when the translational temperature of the gas is high, in which case, there are significant probabilities for the vibrational energy to change over widely separated energy levels after a collision. Data from quasi-classical trajectory simulations of the N + N2 system with ab initio potential energies suggest that the transition probability dependence on the collisional energy possesses an "activation-saturation" behavior and can be described by a simple model. The model allows for explicit evaluation of the vibrational state-to-state transition rate coefficients, from which the evolution of the vibrational energy distribution from any initial conditions can be solved by using the master equation approach. An example of the vibrational energy relaxation in the N + N2 system mimicking the gas behind strong shocks in a hypersonic flow is shown and the results are in good agreement with the available data.

Accurate fundamental invariant-neural network representation of ab initio potential energy surfaces

Highly accurate potential energy surfaces are critically important for chemical reaction dynamics. The large number of degrees of freedom and the intricate symmetry adaption pose a big challenge to accurately representing potential energy surfaces (PESs) for polyatomic reactions. Recently, our group has made substantial progress in this direction by developing the fundamental invariant-neural network (FI-NN) approach. Here, we review these advances, demonstrating that the FI-NN approach can represent highly accurate, global, full-dimensional PESs for reactive systems with even more than 10 atoms. These multi-channel reactions typically involve many intermediates, transition states, and products. The complexity and ruggedness of this potential energy landscape present even greater challenges for full-dimensional PES representation. These PESs exhibit a high level of complexity, molecular size, and accuracy of fit. Dynamics simulations based on these PESs have unveiled intriguing and novel reaction mechanisms, providing deep insights into the intricate dynamics involved in combustion, atmospheric, and organic chemistry.

A highly accurate full-dimensional ab initio potential surface for the rearrangement of methylhydroxycarbene (H3C-C-OH)

We report here a full-dimensional machine learning global potential surface (PES) for the rearrangement of methylhydroxycarbene (H₃C-C-OH, 1t). The PES is trained with the fundamental invariant neural network (FI-NN) method on 91 564 ab initio energies calculated at the UCCSD(T)-F12a/cc-pVTZ level of theory, covering three possible product channels. FI-NN PES has the correct symmetry properties with respect to permutation of four identical hydrogen atoms and is suitable for dynamics studies of the 1t rearrangement. The averaged root mean square error (RMSE) is 11.4 meV. Six important reaction pathways, as well as the energies and vibrational frequencies at the stationary geometries on these pathways are accurately preproduced by our FI-NN PES. To demonstrate the capacity of the PES, we calculated the rate coefficient of hydrogen migration in -CH₃ (path A) and hydrogen migration of -OH (path B) with instanton theory on this PES. Our calculations predicted the half-life of 1t to be 95 min, which is excellent in agreement with experimental observations.

Unexpected steric hindrance failure in the gas phase F- + (CH3)3CI SN2 reaction

Base-induced elimination (E2) and bimolecular nucleophilic substitution (S_N2) reactions are of significant importance in physical organic chemistry. The textbook example of the retardation of S_N2 reactivity by bulky alkyl substitution is widely accepted based on the static analysis of molecular structure and steric environment. However, the direct dynamical evidence of the steric hindrance of S_N2 from experiment or theory remains rare. Here, we report an unprecedented full-dimensional (39-dimensional) machine learning-based potential energy surface for the 15-atom F⁻ + (CH₃)₃CI reaction, facilitating the reliable and efficient reaction dynamics simulations that can reproduce well the experimental outcomes and examine associated atomic-molecular level mechanisms. Moreover, we found surprisingly high “intrinsic” reactivity of S_N2 when the E2 pathway is completely blocked, indicating the reaction that intends to proceed via E2 transits to S_N2 instead, due to a shared pre-reaction minimum. This finding indicates that the competing factor of E2 but not the steric hindrance determines the small reactivity of S_N2 for the F⁻ + (CH₃)₃CI reaction. Our study provides new insight into the dynamical origin that determines the intrinsic reactivity in gas-phase organic chemistry.

Base-induced elimination (E2) and bimolecular nucleophilic substitution (SN2) are of significant importance in physical organic chemistry. Here, the authors show that the competing factor of E2 as opposed to steric hindrance determines the low reactivity of SN2 in the F⁻ + (CH₃)₃CI reaction.