Chemical Space of Molecular Nanomotors: Optimizing Photochemical Properties for One- and Two-photon Applications
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| Format: | Preprint |
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2025
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| _version_ | 1866911079350665216 |
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| author | Mielke, Alexander Scrimgeour, Alexander Tapavicza, Enrico |
| author_facet | Mielke, Alexander Scrimgeour, Alexander Tapavicza, Enrico |
| contents | Light-driven molecular nanomotors hold promise for applications in material science and biomedicine. Significant efforts have focused on improving their efficiency, often targeting single candidate molecules. Here, we present a systematic data-driven approach to design nanomotors with high isomerization quantum yields for one- and two-photon applications, the latter being critical for biomedical applications requiring near-infrared light. We analyze the excited state properties of a dataset of 2016 nanomotors substituted with electron-donating and electron-withdrawing (push-pull) groups. Among the the top candidates, we achieved an increase in two-photon absorption strengths of up to two orders of magnitude compared to existing nanomotors. To ensure that the pi-pi*-character of the excited state is preserved, which is necessary to achieve the required photoisomerization, we introduce a photoreactivity score, that gauges the excited state character based on the transition. Furthermore, we benchmark three machine learning (ML) models Kernel Ridge Regression, XGBoost, and a Neural Network using physical and connectivity-based molecular descriptors. The excellent accuracy of our ML predictions holds promise to replace computationally costly quantum chemistry calculations in chemical space explorations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20328 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Chemical Space of Molecular Nanomotors: Optimizing Photochemical Properties for One- and Two-photon Applications Mielke, Alexander Scrimgeour, Alexander Tapavicza, Enrico Chemical Physics Light-driven molecular nanomotors hold promise for applications in material science and biomedicine. Significant efforts have focused on improving their efficiency, often targeting single candidate molecules. Here, we present a systematic data-driven approach to design nanomotors with high isomerization quantum yields for one- and two-photon applications, the latter being critical for biomedical applications requiring near-infrared light. We analyze the excited state properties of a dataset of 2016 nanomotors substituted with electron-donating and electron-withdrawing (push-pull) groups. Among the the top candidates, we achieved an increase in two-photon absorption strengths of up to two orders of magnitude compared to existing nanomotors. To ensure that the pi-pi*-character of the excited state is preserved, which is necessary to achieve the required photoisomerization, we introduce a photoreactivity score, that gauges the excited state character based on the transition. Furthermore, we benchmark three machine learning (ML) models Kernel Ridge Regression, XGBoost, and a Neural Network using physical and connectivity-based molecular descriptors. The excellent accuracy of our ML predictions holds promise to replace computationally costly quantum chemistry calculations in chemical space explorations. |
| title | Chemical Space of Molecular Nanomotors: Optimizing Photochemical Properties for One- and Two-photon Applications |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2507.20328 |