Roaming in highly excited states: The central atom elimination of triatomic molecule decomposition

Abstract

Chemical reactions are generally assumed to proceed from reactants to products along the minimum energy path (MEP). However, straying from the MEP—roaming—has been recognized as an unconventional reaction mechanism and found to occur in both the ground and first excited states. Its existence in highly excited states is however not yet established. We report a dissociation channel to produce electronically excited fragments, S( 1 D)+O 2 (a 1 Δ g ), from SO 2 photodissociation in highly excited states. The results revealed two dissociation pathways: One proceeds through the MEP to produce vibrationally colder O 2 (a 1 Δ g ) and the other yields vibrationally hotter O 2 (a 1 Δ g ) by means of a roaming pathway involving an intramolecular O abstraction during reorientation motion. Such roaming dynamics may well be the rule rather than the exception for molecular photodissociation through highly excited states.

Surprising new dynamics phenomena in Diels–Alder reaction of C60 uncovered with AI

“…[11][12][13] AI has been instrumental in understanding and discovering interesting mechanistic details in chemical reactions such as roaming. [14][15][16][17] The bottleneck of such AI studies is the construction of robust machine learning potentials and many solutions based on active learning (AL) were suggested to overcome it. [18][19][20][21][22][23][24][25] One of these solutions is our recent efficient physics-informed AL protocol which was able to reproduce the earlier quasi-classical MD investigation 3 of the ethene and 1,3-butadiene at UB3LYP/6-31G* in a fraction of computational cost without requiring any HPC.…”

Surprising new dynamics phenomena in Diels–Alder reaction of C60 uncovered with AI

“…[11][12][13] AI has been instrumental in understanding and discovering interesting mechanistic details in chemical reactions such as roaming. [14][15][16][17] The bottleneck of such AI studies is the construction of robust machine learning potentials and many solutions based on active learning (AL) were suggested to overcome it. [18][19][20][21][22][23][24][25] For example, AL on small prototypical systems contributed to the understanding that in Diels-Alder reactions, the type of performed dynamics (NVE vs NVT or classical vs ring-polymer MD) has a small effect on the outcome.…”

Surprising new dynamics phenomena in Diels–Alder reaction of C60 uncovered with AI

“…[11][12][13] AI has been instrumental in understanding and discovering interesting mechanistic details in chemical reactions such as roaming. [14][15][16][17] The bottleneck of such AI studies is the construction of robust machine learning potentials and many solutions based on active learning (AL) were suggested to overcome it. [18][19][20][21][22][23][24][25] One of these solutions is our recent efficient physics-informed AL protocol which was able to reproduce the earlier quasi-classical MD investigation 3 of the ethene and 1,3-butadiene at UB3LYP/6-31G* in a fraction of computational cost without requiring any HPC.…”