Cargando…
Machine learning prediction of UV–Vis spectra features of organic compounds related to photoreactive potential
Machine learning (ML) algorithms were explored for the classification of the UV–Vis absorption spectrum of organic molecules based on molecular descriptors and fingerprints generated from 2D chemical structures. Training and test data (~ 75 k molecules and associated UV–Vis data) were assembled from...
Autores principales: | Mamede, Rafael, Pereira, Florbela, Aires-de-Sousa, João |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8660842/ https://www.ncbi.nlm.nih.gov/pubmed/34887473 http://dx.doi.org/10.1038/s41598-021-03070-9 |
Ejemplares similares
-
Predicting the UV–vis spectra of oxazine dyes
por: Fleming, Scott, et al.
Publicado: (2011) -
Two excited-state datasets for quantum chemical UV-vis spectra of organic molecules
por: Lupo Pasini, Massimiliano, et al.
Publicado: (2023) -
Machine learning for the prediction of molecular dipole moments obtained by density functional theory
por: Pereira, Florbela, et al.
Publicado: (2018) -
Comparative dataset of experimental and computational attributes of UV/vis absorption spectra
por: Beard, Edward J., et al.
Publicado: (2019) -
Dataset of emission and excitation spectra, UV–vis absorption spectra, and XPS spectra of graphitic C(3)N(4)
por: He, Liangrui, et al.
Publicado: (2018)