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<p>Realizing high-performance perovskite/silicon tandem solar cells requires precise control of wide-bandgap perovskite crystallization. Solvent engineering is the most direct lever for this task; yet, its intricate, multi-variable mechanisms defy intuition-driven design. Herein, we overcome this bottleneck by pioneering a retrieval-augmented large language model to screen &gt; 8000 solvents, identifying <i>γ</i>-valerolactone (GVL) as a non-toxic, high-performance cosolvent. It is found that the GVL strongly coordinates FA<sup>+</sup>, thus precisely modulating crystallization kinetics, retarding nucleation, and promoting oriented, micrometer-scale grain growth. The resulting films exhibit not only superior crystallinity, reduced non-radiative recombination, but also improved scalability to large area and the tolerance to increased film thickness. Consequently, both the single-junction and tandem devices achieve efficiencies of 23.3% and 32.5%, respectively, along with excellent stability under moisture and illumination. This study establishes the first artificial intelligence (AI)-guided cosolvent strategy for 1-μm-thick perovskite layers in perovskite/silicon tandem architectures, underscoring the transformative role of generative AI in advancing high-performance photovoltaics.</p>

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Artificial Intelligence-Guided Cosolvent Design for High-Performance Perovskite/Silicon Tandem Solar Cells

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月21日

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  • Lu Liu,
  • Xinying Cai,
  • Bita Farhadi,
  • Xinrui Dong,
  • Kai Wang,
  • Yufei Shao,
  • Shulin Wang,
  • Jiaxue You,
  • Wanyi Li,
  • Hao-Chung Kuo,
  • Hanying Wang,
  • Dong Yang,
  • Alex K.-Y. Jen,
  • Shengzhong Frank Liu

摘要

Realizing high-performance perovskite/silicon tandem solar cells requires precise control of wide-bandgap perovskite crystallization. Solvent engineering is the most direct lever for this task; yet, its intricate, multi-variable mechanisms defy intuition-driven design. Herein, we overcome this bottleneck by pioneering a retrieval-augmented large language model to screen > 8000 solvents, identifying γ-valerolactone (GVL) as a non-toxic, high-performance cosolvent. It is found that the GVL strongly coordinates FA+, thus precisely modulating crystallization kinetics, retarding nucleation, and promoting oriented, micrometer-scale grain growth. The resulting films exhibit not only superior crystallinity, reduced non-radiative recombination, but also improved scalability to large area and the tolerance to increased film thickness. Consequently, both the single-junction and tandem devices achieve efficiencies of 23.3% and 32.5%, respectively, along with excellent stability under moisture and illumination. This study establishes the first artificial intelligence (AI)-guided cosolvent strategy for 1-μm-thick perovskite layers in perovskite/silicon tandem architectures, underscoring the transformative role of generative AI in advancing high-performance photovoltaics.