Cargando…
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...
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: | |
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 |
Ejemplares similares
-
Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics
por: Zhou, Guoqing, et al.
Publicado: (2022) -
The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules
por: Smith, Justin S., et al.
Publicado: (2020) -
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
por: Smith, Justin S., et al.
Publicado: (2019) -
Machine Learning
Models Capture Plasmon Dynamics in
Ag Nanoparticles
por: Habib, Adela, et al.
Publicado: (2023) -
Automated discovery of a robust interatomic potential for aluminum
por: Smith, Justin S., et al.
Publicado: (2021)