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Interpretable Deep-Learning Unveils Structure–Property Relationships in Polybenzenoid Hydrocarbons
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Abstract
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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Metadata
- Authors
- Weiss, T., Wahab, A., Bronstein, A. M., & Gershoni-Poranne, R.
- Affiliations
- Technion – Israel Institute of Technology (IL)
- ETH Zurich (CH)
- Schulich Faculty of Chemistry, Technion - Israel Institute of Technology, Haifa32000, Israel
- Venue
- The Journal of Organic Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2023-01-25
- Volume
- 88
- Issue
- 14
- Pages
- 9645-9656
- ISSN
- 0022-3263 (ISSN-L)
- 1520-6904
- OpenAlex
- W4318035833
- Cited by
- 24
- References
- 69
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 9 · 2023: 4
- FWCI
- 1.70
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- The Journal of Organic Chemistry (publishedVersion)
- ChemRxiv (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- H2020 European Research Council
- Branco Weiss Fellowship - Society in Science
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Properties of Aromatic Compounds
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- structure-property relationships, HOMO-LUMO gap, relative stability, structural motifs, torsional strain, dispersion effects, nonplanar molecules, explainable deep learning, molecular design
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