Deciphering Dynamics of the Cl + SiH₄ → H + SiH₃Cl Reaction on a Machine Learning Made Globally Accurate Full-Dimensional Potential Energy Surface

Abstract

Chemical reaction dynamics needs the joint effort from both experiment and theory, and theory is useful to rationalize the experimental results by offering intimate details of chemical reaction dynamics and to explore new reaction pathways. With the aid of machine learning, we develop here an accurate fulldimensional potential energy surface (PES) for the reaction between Cl + SiH 4 . This PES can describe well the hydrogen abstraction channel to HCl + SiH 3 . It can also give a good description for the hydrogen substitution channel to H + SiH 3 Cl, which is the focus of the current study and has never been reported by theory. The dynamics of this substitution channel is revealed in detail by calculating ample quasi-classical trajectories (QCTs) on the new PES. The computed product angular distributions are in good agreement with the only crossed molecular beam experiment. Both theory and experiment suggest that this channel takes place mainly via the typical S N 2 inversion mechanism. Theory reveals that there also exists a novel torsion mechanism for the substitution channel. Two dynamic mechanisms are analyzed in detail. The present detailed theoretical dynamics study sheds insightful and novel understanding for this fundamentally important chemical reaction.

Neural Network Based ∆-Machine learning approach efficiently brings the DFT potential energy surface to the CCSD(T) quality: a case for the OH + CH3OH reaction

“…To develop a high-fidelity full-dimensional PES, the first key is to choose a suitable electronic structure method, which should be sufficiently accurate, time-consuming affordably, and provide reliable descriptions for all dynamically relevant regions, including reactants, products, transition states and intermediates, and regions far from the equilibrium of these stationary points. 80,83 The UCCSD(T)-F12a/AVTZ method has succeeded in developing a series of PESs for reactive and non-reactive systems, 64,66,67,84,85 and was suggested for effective construction of full-dimensional accurate PESs. 86 The energies, geometries and harmonic frequencies of the stationary points along R1 and R2 were determined at the level of UCCSD(T)-F12a/AVTZ as implemented in the Molpro program package.…”

“…58 PIP or FI can guarantee the rigorous symmetry property with respect to the permutation of identical atoms, and NN can ensure the high accuracy of the PES. PIP-NN or FI-NN has been successfully employed in fitting full-dimensional accurate PES of many chemical systems 7,9,[59][60][61][62][63][64][65][66][67] based on ample points calculated at the UCCSD(T)-F12a/AVTZ level.…”

Neural Network Based ∆-Machine learning approach efficiently brings the DFT potential energy surface to the CCSD(T) quality: a case for the OH + CH3OH reaction

“…To develop a high-fidelity full-dimensional PES, the first key is to choose a suitable electronic structure method, which should be sufficiently accurate, time-consuming affordably, and provide reliable descriptions for all dynamically relevant regions, including reactants, products, transition states and intermediates, and regions far from the equilibrium of these stationary points. 79,82 The UCCSD(T)-F12a/AVTZ method has succeeded in developing a series of PESs for reactive and non-reactive systems, 63,65,66,83,84 and was suggested for effective construction of full-dimensional accurate PESs. 85 The energies, geometries and harmonic frequencies of the stationary points along R1 and R2 were determined at the level of UCCSD(T)-F12a/AVTZ as implemented in the Molpro program package.…”

“…57 PIP or FI can guarantee the rigorous symmetry property with respect to the permutation of identical atoms, and NN can ensure the high accuracy of the PES. PIP-NN or FI-NN has been successfully employed in fitting full-dimensional accurate PES of many chemical systems 7,9,[58][59][60][61][62][63][64][65][66] based on ample points calculated at the UCCSD(T)-F12a/AVTZ level.…”

The neural network based Δ-machine learning approach efficiently brings the DFT potential energy surface to the CCSD(T) quality: a case for the OH + CH3OH reaction

“…63 PIP or FI can guarantee the rigorous symmetry properties with respect to the permutation of identical atoms, and NN can ensure the high accuracy of the PES. PIP-NN or FI-NN has been successfully employed in fitting full-dimensional accurate PES of many chemical systems 7,9,[64][65][66][67][68][69][70][71][72] based on ample points calculated at the UCCSD(T)-F12a/AVTZ level.…”