Machine Learning Framework for Modeling Exciton-Polaritons in Molecular Materials
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866913190815727616 |
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| author | Li, Xinyang Lubbers, Nicholas Tretiak, Sergei Barros, Kipton Zhang, Yu |
| author_facet | Li, Xinyang Lubbers, Nicholas Tretiak, Sergei Barros, Kipton Zhang, Yu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_02523 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Machine Learning Framework for Modeling Exciton-Polaritons in Molecular Materials Li, Xinyang Lubbers, Nicholas Tretiak, Sergei Barros, Kipton Zhang, Yu Chemical Physics 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. |
| title | Machine Learning Framework for Modeling Exciton-Polaritons in Molecular Materials |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2306.02523 |