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Molecular passivation is an effective strategy to suppress interfacial defects in perovskite solar cells (PSCs), yet the discovery of new passivation molecules remains limited by empirical design and narrow chemical libraries. Here, for the first time, we present an AI-driven framework integrating discriminative and generative language models to accelerate the discovery of effective passivators. A SMILES-X classifier trained on literature data achieved high predictive performance (F1 = 0.80, ROC–AUC = 0.88), while a GPT-2-based generative model iteratively produced over 100 000 novel molecules, more than 80% of which were predicted to be effective. Multi-criteria filtering reduced this pool to ∼8000 high-quality candidates, from which clustering analysis identified ten diverse representatives. Three molecules, including a surrogate analog, were prioritized for experimental testing, and all exhibited a clear passivation effect. In particular, 4-maleimidobutyric acid increased the average open-circuit voltage from 1.08 to 1.12 V and improved the average power conversion efficiency from 19.3% to 22.2%, while markedly reducing hysteresis. This study demonstrates that generative AI can autonomously propose synthetically accessible, functionally effective molecules for PSC passivation, offering a powerful paradigm for accelerated materials discovery beyond conventional chemical space exploration.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Generative AI, Passivation Molecules, Perovskite Solar Cells

刊行年月日: 2026-04-02

出版者: Wiley

掲載誌:

• Advanced Science (ISSN: 21983844) vol. 13 issue. 36 e23042

研究助成金:

• International Institute for Carbon-Neutral Energy Research, Kyushu University WPI‐I2CNER

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1002/advs.202523042

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更新時刻: 2026-07-02 14:43:51 +0900

MDRでの公開時刻: 2026-07-02 16:32:53 +0900