Exploring the Global Reaction Coordinate for Retinal Photoisomerization: A Graph Theory-Based Machine Learning Approach

20 November 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Unraveling the reaction pathway of photoinduced reactions poses a great challenge owing to its complexity. Recently, graph theory-based machine learning combined with non-adiabatic molecular dynamics (NAMD) has been applied to obtain the global reaction coordinate of the photoisomerization of azobenzene. However, NAMD simulations are computationally expensive as they require calculating the non-adiabatic coupling vectors at each time step. Here we showed that ab initio molecular dynamics (AIMD) can be used as an alternative to NAMD by choosing an appropriate initial condition for the simulation. We applied our methodology to determine a plausible global reaction coordinate of retinal photoisomerization, which is essential for human vision. On rank-ordering the internal coordinates, based on the mutual information (MI) between the internal coordinates and the HOMO energy, NAMD and AIMD give a similar trend. Our results demonstrate that instead of NAMD, AIMD-based machine learning is a computationally more affordable approach to study reaction coordinates.


Machine learning
Graph theory
Mutual information
Reaction pathway
Ab-initio molecular dynamics
Non-adiabatic molecular dynamics

Supplementary materials

Supporting Information: Exploring the Global Reaction Coordinate for Retinal Photoisomerization for A Graph Theory-Based Machine Learning Approach
Optimized geometry, atom labels and connectivities of cis and trans retinal.


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