  A light-matter hybrid quasiparticle, called a polariton, is formed when
molecules are strongly coupled to an optical cavity. Recent experiments have
shown that polariton chemistry can manipulate chemical reactions. Polariton
chemistry is a collective phenomenon and its effects increase with the number
of molecules in a cavity. However, simulating an ensemble of molecules in the
excited state coupled to a cavity mode is theoretically and computationally
challenging. Recent advances in machine learning techniques have shown
promising capabilities in modeling ground state chemical systems. This work
presents a general protocol to predict excited-state properties, such as
energies, transition dipoles, and non-adiabatic coupling vectors with the
hierarchically interacting particle neural network. Machine learning
predictions are then applied to compute potential energy surfaces and
electronic spectra of a prototype azomethane molecule in the collective
coupling scenario. These computational tools provide a much-needed framework to
model and understand many molecules' emerging excited-state polariton
chemistry.
