Data‐Driven Discovery of Quaternary Ammonium Interlayers for Efficient and Thermally Stable Perovskite Solar Cells

Interfacial engineering is essential for improving charge extraction and suppressing non‐radiative recombination in perovskite solar cells (PSCs). Although numerous organic interfacial materials (IMs) have been explored, the vast molecular design space renders purely experimental screening inefficient. Here, we report on a machine learning‐based framework that rapidly screens IMs using an in‐house database. Six physicochemical descriptors capturing perovskite–molecule interactions were selected to train a Gaussian Process Regression model embedded in a Bayesian Optimization active learning loop. Post hoc interpretability revealed that thermally robust, higher‐order alkylammonium cations are particularly beneficial for PSC interfaces. The model nominated 15 promising, previously untested IMs; one of them, tetra‐n‐hexyl‐ammonium bromide, was experimentally incorporated into PSCs. Devices treated with this IM delivered a power‐conversion efficiency of 25.31% under AM 1.5 G illumination and, remarkably, retained about 81.6% of the initial efficiency after 1508 h at 85°C, demonstrating enhanced thermal stability. These results demonstrate how an interpretable, data‐driven strategy can accelerate the rational discovery of IMs, enabling the development of PSCs that combine record‐level efficiency with outstanding long‐term stability.