Machine Learning Framework for Modeling Exciton-Polaritons in Molecular Materials

TL;DR

This paper introduces a machine learning framework to efficiently model excited-state properties of molecules strongly coupled to optical cavities, enabling better understanding of polariton chemistry in collective molecular systems.

Contribution

It develops a neural network-based protocol to predict excited-state properties and simulate potential energy surfaces for molecules in cavity environments, addressing computational challenges.

Findings

Accurately predicts excited-state energies and transition dipoles.

Computes potential energy surfaces and spectra for azomethane.

Provides a scalable framework for modeling polariton chemistry.

Abstract

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.