Machine Learning-Based Quantitative Structure–Property Relationships for the Electronic Properties of Cyano Polycyclic Aromatic Hydrocarbons

[Image: see text] In this study, quantitative structure–property relationships (QSPR) based on a machine learning (ML) methodology and the truncated degree of π-orbital overlap (DPO) to predict the electronic properties, namely, the bandgaps, electron affinities, and ionization potentials of the cya...

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Detalles Bibliográficos
Autores principales: Nguyen, Tuan H., Le, Khang M., Nguyen, Lam H., Truong, Thanh N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9835191/
https://www.ncbi.nlm.nih.gov/pubmed/36643419
http://dx.doi.org/10.1021/acsomega.2c05159

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9835191/
https://www.ncbi.nlm.nih.gov/pubmed/36643419
http://dx.doi.org/10.1021/acsomega.2c05159

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