ArticleACS omega2023
Machine Learning-Based Quantitative Structure-Property Relationships for the Electronic Properties of Cyano Polycyclic Aromatic Hydrocarbons.
Article in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Quantum mechanical and machine learning prediction of rotational energy barriers in halogenated aromatic alcohols.Journal of molecular modeling · 2025Article
- Atom-Based Machine Learning Model for Quantitative Property-Structure Relationship of Electronic Properties of Fusenes and Substituted Fusenes.ACS omega · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 cyano polycyclic aromatic hydrocarbon (CN-PAH) chemical class were developed. The level of theory B3LYP/6-31+G(d) of density functional theory (DFT) was used to calculate a total of 926 data points for the development of the QSPR model. To include the substituents effects, a new descriptor was added to the DPO model. Consequently, the new ML-DPO model yields excellent linear correlations to predict the desired electronic properties with high accuracy to within 0.2 eV for all multi-CN-substituted PAHs and 0.1 eV for the mono-CN-substituted PAH subclass.
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Registered trials
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