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<p>Organic photosensitizers (PSs) are central to light-driven photochemical and photobiological processes across biomedicine, energy, environment and synthetic photochemistry, owing to their tunable molecular structures and versatile excited-state dynamics. The photosensitization mechanisms of organic PSs can be broadly classified into oxygen-dependent type II, less oxygen-dependent type I and emerging oxygen-independent pathways. Building on these mechanistic insights, diverse molecular design strategies have been developed to fine-tune excited-state processes and to improve photosensitization efficiency. However, the incomplete understanding of structure–property relationships has motivated the integration of machine learning as a data-driven and predictive framework to streamline candidate discovery and optimization. In this Review, we first summarize the molecular design guidelines for type II, type I and oxygen-independent PSs, followed by a discussion of strategies that modulate the photosensitization pathways. We then examine the machine-learning approaches for predictive PS discovery. Finally, we outline the challenges in designing high-performance PSs and future directions for machine learning towards more reliable and intelligent PS discovery.</p>

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Molecular design for high-performance organic photosensitizers

  • 期刊论文

  • 发表日期：

    2026年5月29日

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  • Jiahao Zhuang,
  • Yufu Tang,
  • Yixin Zhu,
  • Xia Ling,
  • Bin Liu

摘要

Organic photosensitizers (PSs) are central to light-driven photochemical and photobiological processes across biomedicine, energy, environment and synthetic photochemistry, owing to their tunable molecular structures and versatile excited-state dynamics. The photosensitization mechanisms of organic PSs can be broadly classified into oxygen-dependent type II, less oxygen-dependent type I and emerging oxygen-independent pathways. Building on these mechanistic insights, diverse molecular design strategies have been developed to fine-tune excited-state processes and to improve photosensitization efficiency. However, the incomplete understanding of structure–property relationships has motivated the integration of machine learning as a data-driven and predictive framework to streamline candidate discovery and optimization. In this Review, we first summarize the molecular design guidelines for type II, type I and oxygen-independent PSs, followed by a discussion of strategies that modulate the photosensitization pathways. We then examine the machine-learning approaches for predictive PS discovery. Finally, we outline the challenges in designing high-performance PSs and future directions for machine learning towards more reliable and intelligent PS discovery.