Ultrafast Photocontrolled Rotation in a Molecular Motor Investigated by Machine Learning-Based Nonadiabatic Dynamics Simulations

The thermal helix inversion (THI) of the overcrowded
alkene-based
molecular motors determines the speed of the unidirectional rotation
due to the high reaction barrier in the ground state, in comparison
with the ultrafast photoreaction process. Recently, a phosphine-based
motor has achieved all-photochemical rotation experimentally, promising
to be controlled without a thermal step. However, the mechanism of
this photochemical reaction has not yet been fully revealed. The comprehensive
computational studies on photoisomerization still resort to nonadiabatic
molecular dynamics (NAMD) simulations based on electronic structure
calculations, which remains a high computational cost for large systems
such as molecular motors. Machine learning (ML) has become an accelerating
tool in NAMD simulations recently, where excited-state potential energy
surfaces (PESs) are constructed analytically with high accuracy, providing
an efficient approach for simulations in photochemistry. Herein the
reaction pathway is explored by a spin-flip time-dependent density
functional theory (SF-TDDFT) approach in combination with ML-based
NAMD simulations. According to our computational simulations, we notice
that one of the key factors of fulfilling all-photochemical rotation
in the phosphine-based motor is that the excitation energies of four
isomers are similar. Additionally, a shortcut photoinduced transformation
between unstable isomers replaces the THI step, which shares the conical
intersection (CI) with photoisomerization. In this study, we provide
a practical approach to speed up the NAMD simulations in photochemical
reactions for a large system that could be extended to other complex
systems.