Modeling Excited-State Dynamics for Polariton Chemistry with Hierarchically Interacting Particle Neural Network

Chemical reactions inside an optical cavity happen intrinsically in the collective coupling regimes, where many molecules couple to the same cavity simultaneously. However, modeling such a complicated system with an assemble of molecules is computationally expensive. Recently, machine learning (ML) techniques, especially neural networks (NN), have been widely used to predict quantities, like energies and charges. As such, it is a natural choice to combine NN with polariton chemistry simulations. Unfortunately, even for a single molecule, predicting excited states quantities with NN remains a challenging task. In this talk, we present a general protocol to predict excited-state properties, such as energies, transition dipoles, and non-adiabatic coupling vectors (NACR) with the hierarchically interacting particle neural network (HIP-NN), and applying these predictions to excited-state polariton chemistry calculation in the collective coupling regime.