The COMPAS Project: A Computational Database of Polycyclic Aromatic Systems. Phase 1: <i>cata</i>-Condensed Polybenzenoid Hydrocarbons

Wahab, Alexandra; Pfuderer, Lara; Paenurk, Eno; Gershoni‐Poranne, Renana

doi:10.1021/acs.jcim.2c00503

Cited by 21 publications

(35 citation statements)

References 61 publications

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“…The application of these two new tools to the newly reported database, COMPAS-1D, [36] allowed us to gain chemical insight into the structure-property relationships of PBHs and into the utility of the new tools. The following main conclusions were reached:…”

Section: Discussionmentioning

confidence: 99%

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Text‐based representations with interpretable machine learning reveal structure–property relationships of polybenzenoid hydrocarbons

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Wahab

Paenurk

et al. 2022

J of Physical Organic Chem

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New tools are developed and applied to enable the use of interpretable machine learning for investigation of structure–property relationships in polybenzenoid hydrocarbons (PBHs). A textual molecular representation, which is based on the annulation sequence of PBHs, is shown to be of utility either in its textual form or as a basis for a curated feature vector. Both forms display interpretability exceeding those achievable by standard SMILES representation; and the former also has increased predictive accuracy. A recently developed model, CUSTODI, was applied for the first time as an interpretable model, identifying important structural features that impact various electronic molecular properties. The resulting insights not only validate several well‐known “rules of thumb” of organic chemistry but also reveal new behaviors and influential structural motifs, thus providing guiding principles for rational design and fine‐tuning of PBHs.

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Section: Discussionmentioning

confidence: 99%

“…We then automated the process to generate both types of representations (vide infra for further details) for the ~8600 molecules in the COMPAS‐1D dataset. [ 36 ]…”

Section: Methodsmentioning

confidence: 99%

Text‐based representations with interpretable machine learning reveal structure–property relationships of polybenzenoid hydrocarbons

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Wahab

Paenurk

et al. 2022

J of Physical Organic Chem

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“…In addition, PBHs containing linear chains longer than six rings were discarded, as these are known to have biradical character. For further details on the benchmarking and data generation pipeline, we refer the reader to ref . In section S1 of the Supporting Information, we provide additional information on the suitability of the level of theory and the electronic structure of the studied compounds.…”

Section: Methodsmentioning

confidence: 99%

“…■ COMPUTATIONAL METHODS Data. The COMPAS-1D data set of the COMPAS Project 55 was used. The data set contains the density functional theory-calculated structures and properties of ∼9K cata-condensed PBHs comprising 1−10 rings.…”

Section: ■ Conclusionmentioning

confidence: 99%

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Interpretable Deep-Learning Unveils Structure–Property Relationships in Polybenzenoid Hydrocarbons

et al. 2023

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In this work, interpretable deep learning was used to identify structure−property relationships governing the HOMO− LUMO gap and the relative stability of polybenzenoid hydrocarbons (PBHs) using a ring-based graph representation. This representation was combined with a subunit-based perception of PBHs, allowing chemical insights to be presented in terms of intuitive and simple structural motifs. The resulting insights agree with conventional organic chemistry knowledge and electronic structure-based analyses and also reveal new behaviors and identify influential structural motifs. In particular, we evaluated and compared the effects of linear, angular, and branching motifs on these two molecular properties and explored the role of dispersion in mitigating the torsional strain inherent in nonplanar PBHs. Hence, the observed regularities and the proposed analysis contribute to a deeper understanding of the behavior of PBHs and form the foundation for design strategies for new functional PBHs.

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A Crowning Achievement: The First Solution‐Phase Synthesis of Circumcoronenes

Gershoni‐Poranne

Tsybizova

2023

Angewandte Chemie

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Circumcoronene is a highly symmetric polybenzenoid hydrocarbon that has fascinated organic chemists and materials scientists for decades. However, until recently, it had only been studied theoretically, because its synthesis in solution remained challenging. In a recent report, Wu and co-workers described the first successful solution-phase synthesis and isolation of crystalline circumcoronene derivatives, validating the long-predicted Clar structure. Their synthetic approach enables preparation of new polycyclic benzenoid hydrocarbons with multiple K-regions, which are difficult to obtain, and expands our understanding of the chemistry of these systems.

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The COMPAS Project: A Computational Database of Polycyclic Aromatic Systems. Phase 1: cata-Condensed Polybenzenoid Hydrocarbons

Cited by 21 publications

References 61 publications

Text‐based representations with interpretable machine learning reveal structure–property relationships of polybenzenoid hydrocarbons

Text‐based representations with interpretable machine learning reveal structure–property relationships of polybenzenoid hydrocarbons

Interpretable Deep-Learning Unveils Structure–Property Relationships in Polybenzenoid Hydrocarbons

A Crowning Achievement: The First Solution‐Phase Synthesis of Circumcoronenes

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