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Excited-State Distortions Promote the Photochemical 4π-Electrocyclizations of Fluorobenzenes via Machine Learning Accelerated Photodynamics Simulations
Authors:Dr Jingbai Li  Prof Steven A Lopez
Institution:Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, 02115 USA
Abstract:Benzene fluorination increases chemoselectivities for Dewar-benzenes via 4π-disrotatory electrocyclization. However, the origin of the chemo- and regioselectivities of fluorobenzenes remains unexplained because of the experimental limitations in resolving the excited-state structures on ultrafast timescales. The computational cost of multiconfigurational nonadiabatic molecular dynamics simulations is also currently cost-prohibitive. We now provide high-fidelity structural information and reaction outcome predictions with machine-learning-accelerated photodynamics simulations of a series of fluorobenzenes, C6F6-nHn, n=0–3, to study their S1→S0 decay in 4 ns. We trained neural networks with XMS-CASPT2(6,7)/aug-cc-pVDZ calculations, which reproduced the S1 absorption features with mean absolute errors of 0.04 eV (<2 nm). The predicted nonradiative decay constants for C6F4H2, C6F6, C6F3H3, and C6F5H are 116, 60, 28, and 12 ps, respectively, in broad qualitative agreement with the experiments. Our calculations show that a pseudo Jahn–Teller distortion of fluorinated benzenes leads to an S1 local-minimum region that extends the excited-state lifetimes of fluorobenzenes. The pseudo Jahn–Teller distortions reduce when fluorination decreases. Our analysis of the S1 dynamics shows that the pseudo-Jahn–Teller distortions promote an excited-state cis-trans isomerization of a πC-C bond. We characterized the surface hopping points from our NAMD simulations and identified instantaneous nuclear momentum as a factor that promotes the electrocyclizations.
Keywords:computational chemistry  electrocyclic reactions  Jahn–Teller distortions  machine learning nonadiabatic molecular dynamics  photochemistry
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