Excited-State Distortions Promote the Photochemical 4π-Electrocyclizations of Fluorobenzenes via Machine Learning Accelerated Photodynamics Simulations.
Jingbai LiSteven A LopezPublished in: Chemistry (Weinheim an der Bergstrasse, Germany) (2022)
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, C 6 F 6-n H n , n=0-3, to study their S 1 →S 0 decay in 4 ns. We trained neural networks with XMS-CASPT2(6,7)/aug-cc-pVDZ calculations, which reproduced the S 1 absorption features with mean absolute errors of 0.04 eV (<2 nm). The predicted nonradiative decay constants for C 6 F 4 H 2 , C 6 F 6 , C 6 F 3 H 3 , and C 6 F 5 H 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 S 1 local-minimum region that extends the excited-state lifetimes of fluorobenzenes. The pseudo Jahn-Teller distortions reduce when fluorination decreases. Our analysis of the S 1 dynamics shows that the pseudo-Jahn-Teller distortions promote an excited-state cis-trans isomerization of a π C-C bond. We characterized the surface hopping points from our NAMD simulations and identified instantaneous nuclear momentum as a factor that promotes the electrocyclizations.
Keyphrases
- molecular dynamics simulations
- molecular dynamics
- machine learning
- monte carlo
- neural network
- density functional theory
- photodynamic therapy
- molecular docking
- artificial intelligence
- big data
- high resolution
- electron transfer
- resistance training
- deep learning
- patient safety
- systematic review
- healthcare
- social media
- dengue virus
- body composition
- high intensity
- rectal cancer