Predicting UV-Vis Spectra of Benzothio/Dithiophene Polymers for Photodetectors by Machine-Learning-Assisted Computational Studies Article Swipe
Abrar U. Hassan
,
Mamduh J. Aljaafreh
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/coatings15050558
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/coatings15050558
The current study represents a machine-learning (ML)-assisted reverse polymer engineering for the rational design of high-performance benzothiophene (BT) benzodithiophene (BDT) polymers for photodetector applications. By integrating their 5617 units with various acceptor moieties, a total of 72,976 unique polymer combinations are generated. The optical properties of these polymers are predicted with high accuracy (R2 = 0.86) using a Gradient-Boosting Regression (GBR) model. The SHAP value-based feature importance analysis indicates that Chi0 is the most influential factor in predicting the absorption maxima (λmax) of polymers, followed by LabuteASA, Chi0V, Chi1, SlogP_VSA12, and other molecular descriptors. The robustness of the employed model is further validated through K-Fold cross-validation, with the highest mean squared error (MSE) observed at 2.02 in the fold-2 subset. The designed polymers exhibit λmax within the range of 400–750 nm, demonstrating their suitability for photodetector applications. Moreover, a Transformer-Assisted Orientation (TAO) approach is employed to optimize polymer design, successfully achieving bandgaps as low as 0.42 eV. This approach facilitates the rapid design and optimization of high-performance polymers with tailored electronic properties, effectively addressing the limitations of conventional trial-and-error methods. The current ML-assisted approach presents a promising strategy for expediting the development of high-performance photodetectors and other advanced optoelectronic devices.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/coatings15050558
- https://www.mdpi.com/2079-6412/15/5/558/pdf?version=1746686540
- OA Status
- gold
- Cited By
- 4
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410155983
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410155983Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/coatings15050558Digital Object Identifier
- Title
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Predicting UV-Vis Spectra of Benzothio/Dithiophene Polymers for Photodetectors by Machine-Learning-Assisted Computational StudiesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-07Full publication date if available
- Authors
-
Abrar U. Hassan, Mamduh J. AljaafrehList of authors in order
- Landing page
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https://doi.org/10.3390/coatings15050558Publisher landing page
- PDF URL
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https://www.mdpi.com/2079-6412/15/5/558/pdf?version=1746686540Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2079-6412/15/5/558/pdf?version=1746686540Direct OA link when available
- Concepts
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Photodetector, Polymer, Materials science, Spectral line, Optoelectronics, Physics, Composite material, AstronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.LabuteASA, | 86 |
| abstract_inverted_index.Regression | 59 |
| abstract_inverted_index.absorption | 79 |
| abstract_inverted_index.addressing | 173 |
| abstract_inverted_index.electronic | 170 |
| abstract_inverted_index.expediting | 189 |
| abstract_inverted_index.generated. | 41 |
| abstract_inverted_index.importance | 66 |
| abstract_inverted_index.predicting | 77 |
| abstract_inverted_index.properties | 44 |
| abstract_inverted_index.represents | 3 |
| abstract_inverted_index.robustness | 95 |
| abstract_inverted_index.ML-assisted | 182 |
| abstract_inverted_index.Orientation | 140 |
| abstract_inverted_index.development | 191 |
| abstract_inverted_index.effectively | 172 |
| abstract_inverted_index.engineering | 9 |
| abstract_inverted_index.facilitates | 159 |
| abstract_inverted_index.influential | 74 |
| abstract_inverted_index.integrating | 25 |
| abstract_inverted_index.limitations | 175 |
| abstract_inverted_index.properties, | 171 |
| abstract_inverted_index.suitability | 133 |
| abstract_inverted_index.value-based | 64 |
| abstract_inverted_index.SlogP_VSA12, | 89 |
| abstract_inverted_index.combinations | 39 |
| abstract_inverted_index.conventional | 177 |
| abstract_inverted_index.descriptors. | 93 |
| abstract_inverted_index.optimization | 164 |
| abstract_inverted_index.successfully | 149 |
| abstract_inverted_index.(ML)-assisted | 6 |
| abstract_inverted_index.applications. | 23, 136 |
| abstract_inverted_index.demonstrating | 131 |
| abstract_inverted_index.photodetector | 22, 135 |
| abstract_inverted_index.benzothiophene | 16 |
| abstract_inverted_index.optoelectronic | 198 |
| abstract_inverted_index.photodetectors | 194 |
| abstract_inverted_index.trial-and-error | 178 |
| abstract_inverted_index.benzodithiophene | 18 |
| abstract_inverted_index.high-performance | 15, 166, 193 |
| abstract_inverted_index.machine-learning | 5 |
| abstract_inverted_index.Gradient-Boosting | 58 |
| abstract_inverted_index.cross-validation, | 105 |
| abstract_inverted_index.Transformer-Assisted | 139 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.94678766 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |