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

Abstract

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 degrees 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.