NO-modulated triplet ground state and adaptive antiaromaticity in BN-doped cyclobutadienes: A combined DFT and machine learning study

Sajid Imran; Wenhao Wang; Yuan Gao; Jun Zhu

doi:10.1039/d5ob01131k

January 1, 2025Organic & Biomolecular Chemistry

NO-modulated triplet ground state and adaptive antiaromaticity in BN-doped cyclobutadienes: A combined DFT and machine learning study

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Key Points

The findings reveal novel insights into controlling aromaticity in the triplet state.
Calculations show that antiaromaticity behaviors can be influenced using BN-dopants in cyclobutadienes.
The research employs DFT along with machine learning algorithms for enhanced predictive modeling.
The implications point to new pathways for designing aromatic compounds with unique electronic properties.

Implication

This study combines DFT and machine learning to explore aromaticity and antiaromaticity in novel BN-doped cyclobutadienes.

Abstract

Controlling aromaticity across electronic states is crucial for designing novel species. While aromaticity typically could be achieved in either the lowest singlet state (S0) or the lowest triplet state (T₁),...

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Imran et al. (2025) studied this question.

synapsesocial.com/papers/68de6f3a83cbc991d0a227e7 https://doi.org/10.1039/d5ob01131k

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NO-modulated triplet ground state and adaptive antiaromaticity in BN-doped cyclobutadienes: A combined DFT and machine learning study

Key Points

Implication

Abstract

Discussion

Cite This Study

NO-modulated triplet ground state and adaptive antiaromaticity in BN-doped cyclobutadienes: A combined DFT and machine learning study

Key Points

Implication

Abstract

Discussion

Cite This Study

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