Predicting phosphorescence energies and inferring wavefunction localization with machine learning

Phosphorescence is commonly utilized for applications including light-emitting diodes and photovoltaics. Machine learning (ML) approaches trained on ab initio datasets of singlet–triplet energy gaps may expedite the discovery of phosphorescent compounds with the desired emission energies. However, w...

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Detalles Bibliográficos
Autores principales: Sifain, Andrew E., Lystrom, Levi, Messerly, Richard A., Smith, Justin S., Nebgen, Benjamin, Barros, Kipton, Tretiak, Sergei, Lubbers, Nicholas, Gifford, Brendan J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2021
Materias:
Chemistry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336587/
https://www.ncbi.nlm.nih.gov/pubmed/34447529
http://dx.doi.org/10.1039/d1sc02136b

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336587/
https://www.ncbi.nlm.nih.gov/pubmed/34447529
http://dx.doi.org/10.1039/d1sc02136b

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