Selected machine learning of HOMO–LUMO gaps with improved data-efficiency

Despite their relevance for organic electronics, quantum machine learning (QML) models of molecular electronic properties, such as HOMO–LUMO-gaps, often struggle to achieve satisfying data-efficiency as measured by decreasing prediction errors for increasing training set sizes. We demonstrate that p...

Descripción completa

Detalles Bibliográficos
Autores principales: Mazouin, Bernard, Schöpfer, Alexandre Alain, von Lilienfeld, O. Anatole
Formato: Online Artículo Texto
Lenguaje:English
Publicado: RSC 2022
Materias:
Chemistry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662596/
https://www.ncbi.nlm.nih.gov/pubmed/36561279
http://dx.doi.org/10.1039/d2ma00742h

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662596/
https://www.ncbi.nlm.nih.gov/pubmed/36561279
http://dx.doi.org/10.1039/d2ma00742h

Ejemplares similares