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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...

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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

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