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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
American Chemical Society
2022
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| 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 |