Excited‐State Distortions Promote the Photochemical 4π‐Electrocyclizations of Fluorobenzenes via Machine Learning Accelerated Photodynamics Simulations

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 (6F4H2, 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.

Document Details

Document Type
Pub Defense Publication
Publication Date
May 25, 2022
Source ID
10.1002/chem.202200651

Entities

People

  • Jingbai Li
  • Steven A Lopez

Organizations

  • National Science Foundation Directorate for Mathematical & Physical Sciences
  • Northeastern University
  • Office of Naval Research

Tags

Readers

  • Computational Fluid Dynamics (CFD)
  • Optical Physics and Photonics.
  • Quantum Chemistry

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks