Academic Journal
Machine Learning Framework for Modeling Exciton Polaritons in Molecular Materials
| العنوان: | Machine Learning Framework for Modeling Exciton Polaritons in Molecular Materials |
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| المؤلفون: | Xinyang Li, Nicholas Lubbers, Sergei Tretiak, Kipton Barros, Yu Zhang |
| سنة النشر: | 2024 |
| مصطلحات موضوعية: | Biophysics, Biochemistry, Inorganic Chemistry, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Physical Sciences not elsewhere classified, Information Systems not elsewhere classified, prototype azomethane molecule, potential energy surfaces, nonadiabatic coupling vectors, manipulate chemical reactions, computational tools provide, state chemical systems, modeling exciton polaritons, collective coupling scenario, shown promising capabilities, excited state coupled, machine learning framework, state polariton chemistry, machine learning, state properties, strongly coupled, predict excited, polariton chemistry, needed framework, modeling ground, collective phenomenon, work presents, transition dipoles, recent experiments |
| الوصف: | 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 (ML) 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 nonadiabatic coupling vectors with the hierarchically interacting particle neural network. ML predictions are then applied to compute the 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | unknown |
| Relation: | https://figshare.com/articles/journal_contribution/Machine_Learning_Framework_for_Modeling_Exciton_Polaritons_in_Molecular_Materials/24936630 |
| DOI: | 10.1021/acs.jctc.3c01068.s001 |
| الاتاحة: | https://doi.org/10.1021/acs.jctc.3c01068.s001 https://figshare.com/articles/journal_contribution/Machine_Learning_Framework_for_Modeling_Exciton_Polaritons_in_Molecular_Materials/24936630 |
| Rights: | CC BY-NC 4.0 |
| رقم الانضمام: | edsbas.16EEDE41 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1021/acs.jctc.3c01068.s001 |
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