Large Language Model Guided Discovery of Hole Transport Layer Dopants for Efficient and Stable Perovskite Photovoltaics

ABSTRACT Achieving high efficiency and long‐term stability in n‐i‐p perovskite solar cells (PSCs) remains constrained by the hole transport layer (HTL) dopant chemistry. The most commonly used dopant for 2,2',7,7'‐tetrakis(N,N‐di‐4‐methoxyphenylamino)‐9,9'‐spirobifluorene (Spiro‐OMeTAD), typically based on lithium bis(trifluoromethanesulfonyl)imide, enables state‐of‐the‐art power conversion efficiency (PCE) but often sacrifices thermal and environmental robustness due to hygroscopicity, ionic migration, and reduced glass‐transition temperature. Here, a HTL‐dopant‐focused large language model (LLM) framework is constructed to mine the literature at scale. Using a corpus of over 70 000 publications for retrieval‐guided learning, the model identifies trityl tetrakis(pentafluorophenyl) borate (TrTPFB) as an effective p‐dopant that improves hole transport in Spiro‐OMeTAD, while also improving the morphology and hydrophobicity of the HTL film. With optimized TrTPFB doping concentration, the champion lithium‐free Spiro‐OMeTAD based device reaches a PCE of 24.13%, and retains 92.67% and 85.82% of its initial PCE after 900 h thermal aging at 65°C with 30% RH and at 85°C in N 2 , respectively. This study shows how LLM can turn scattered literature into useful experimental guidance for exploring efficient, stable perovskite photovoltaics.