Machine Learning Framework for Modeling Exciton-Polaritons in Molecular Materials

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.

References in corpus (37)

• On representing chemical environments
• Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
• Moment Tensor Potentials: a class of systematically improvable interatomic potentials
• Machine Learning of Accurate Energy-Conserving Molecular Force Fields
• Cold atoms in cavity-generated dynamical optical potentials
• Comparing molecules and solids across structural and alchemical space
• Machine learning prediction errors better than DFT accuracy
• Conductivity in organic semiconductors hybridized with the vacuum field
• Cavity-controlled chemistry in molecular ensembles
• Atoms and Molecules in Cavities: From Weak to Strong Coupling in QED Chemistry
• Machine learning for electronically excited states of molecules
• Cavity-induced modifications of molecular structure in the strong coupling regime
• Suppressing photochemical reactions with quantized light fields
• Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species
• Hierarchical modeling of molecular energies using a deep neural network
• Unified Representation of Molecules and Crystals for Machine Learning
• Plasmon-Exciton-Polariton Lasing
• Resonant catalysis of thermally-activated chemical reactions via vibrational polaritons
• Quantum theory of collective strong coupling of molecular vibrations with a microcavity mode
• Machine Learning for Quantum Mechanical Properties of Atoms in Molecules
• Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics
• Machine learning enables long time scale molecular photodynamics simulations
• Cavity Born-Oppenheimer Approximation for Correlated Electron-Nuclear-Photon Systems
• Inclusion of machine learning kernel ridge regression potential energy surfaces in on-the-fly nonadiabatic molecular dynamics simulation
• Machine learning of molecular properties: locality and active learning
• Kohn-Sham Approach to Quantum Electrodynamical Density Functional Theory: Exact Time-Dependent Effective Potentials in Real Space
• Non-adiabatic dynamics of molecules in optical cavities
• Can ultrastrong coupling change ground state chemical reactions?
• Exact states and spectra of vibrationally dressed polaritons
• Room temperature Organic Exciton-Polariton Condensate in a Lattice
• Photo-induced electron transfer in the strong coupling regime: Waveguide-plasmon polaritons
• Organic Polaritons Enable Local Vibrations to Drive Long-Range Energy Transfer
• Deep Learning for UV Absorption Spectra with SchNarc: First Steps Towards Transferability in Chemical Compound Space
• Entangled Photonic-Nuclear Molecular Dynamics of LiF in Quantum Optical Cavities
• Non-Adiabatic Molecular Dynamics of Molecules in the Presence of Strong Light-Matter Interactions
• Plasmon mediated coherent population oscillations in molecular aggregates
• Lightweight and Effective Tensor Sensitivity for Atomistic Neural Networks

Cited by in corpus (1)

• A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors