Generative AI-Driven Accelerated Discovery of Passivation Molecules for Perovskite Solar Cells

Abstract

Molecular passivation is an effective strategy to suppress interfacial defects in perovskite solar cells (PSCs), yet the discovery of new passivation molecules remains limited by empirical design and narrow chemical libraries. Here, for the first time, we present an AI-driven framework integrating discriminative and generative language models to accelerate the discovery of effective passivators. A SMILES-X classifier trained on literature data achieved high predictive performance (F1 = 0.80, ROC–AUC = 0.88), while a GPT-2-based generative model iteratively produced over 100 000 novel molecules, more than 80% of which were predicted to be effective. Multi-criteria filtering reduced this pool to ∼8000 high-quality candidates, from which clustering analysis identified ten diverse representatives. Three molecules, including a surrogate analog, were prioritized for experimental testing, and all exhibited a clear passivation effect. In particular, 4-maleimidobutyric acid increased the average open-circuit voltage from 1.08 to 1.12 V and improved the average power conversion efficiency from 19.3% to 22.2%, while markedly reducing hysteresis. This study demonstrates that generative AI can autonomously propose synthetically accessible, functionally effective molecules for PSC passivation, offering a powerful paradigm for accelerated materials discovery beyond conventional chemical space exploration.