From orbital analysis to active learning: an integrated strategy for the accelerated design of TADF emitters
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915657576087552 |
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| author | Njafa, Jean-Pierre Tchapet Kouam, Steve Cabrel Teguia Kongo, Patrick Mvoto Engo, Serge Guy Nana |
| author_facet | Njafa, Jean-Pierre Tchapet Kouam, Steve Cabrel Teguia Kongo, Patrick Mvoto Engo, Serge Guy Nana |
| contents | Thermally Activated Delayed Fluorescence (TADF) emitters must satisfy two competing requirements: small singlet-triplet energy gaps for thermal upconversion and sufficient spin-orbit coupling for fast reverse intersystem crossing. Predicting these properties accurately demands expensive calculations. We address this using a validated semi-empirical protocol (GFN2-xTB geometries, sTDA/sTD-DFT-xTB excited states) on 747 molecules, combined with charge-transfer descriptors from Natural Transition Orbital analysis. The hole-electron spatial overlap She emerges as a key predictor, accounting for 21% of feature importance for the triplet state alone. Our best model (Support Vector Regression) reaches MAE = 0.024 eV and R2 = 0.96 for $ΔE_{ST}$. Active learning reduces the data needed to reach target accuracy by approximately 25% compared to random sampling. Three application domains are explored: NIR-emitting probes for bioimaging, photocatalytic sensitizers, and fast-response materials for photodetection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06029 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From orbital analysis to active learning: an integrated strategy for the accelerated design of TADF emitters Njafa, Jean-Pierre Tchapet Kouam, Steve Cabrel Teguia Kongo, Patrick Mvoto Engo, Serge Guy Nana Materials Science Chemical Physics Thermally Activated Delayed Fluorescence (TADF) emitters must satisfy two competing requirements: small singlet-triplet energy gaps for thermal upconversion and sufficient spin-orbit coupling for fast reverse intersystem crossing. Predicting these properties accurately demands expensive calculations. We address this using a validated semi-empirical protocol (GFN2-xTB geometries, sTDA/sTD-DFT-xTB excited states) on 747 molecules, combined with charge-transfer descriptors from Natural Transition Orbital analysis. The hole-electron spatial overlap She emerges as a key predictor, accounting for 21% of feature importance for the triplet state alone. Our best model (Support Vector Regression) reaches MAE = 0.024 eV and R2 = 0.96 for $ΔE_{ST}$. Active learning reduces the data needed to reach target accuracy by approximately 25% compared to random sampling. Three application domains are explored: NIR-emitting probes for bioimaging, photocatalytic sensitizers, and fast-response materials for photodetection. |
| title | From orbital analysis to active learning: an integrated strategy for the accelerated design of TADF emitters |
| topic | Materials Science Chemical Physics |
| url | https://arxiv.org/abs/2512.06029 |