Authors
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
Published in
Nano-micro letters. Volume 18. Issue 1. Jul 21, 2026. Epub Jul 21, 2026.
Abstract
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.
PMID:
42479286
Bibliographic data and abstract were imported from PubMed on 21 Jul 2026.
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