Beyond rapid nucleation: unveiling the role of solvent–precursor interactions in antisolvent-free perovskite fabrication

Solution-processed antisolvent-free perovskites (AFPs) are promising candidates for scalable photovoltaic (PV) production. However, achieving high-quality AFP films typically requires stringent processing conditions, limiting reliability in large-scale manufacturing. Here, we employ machine learning (ML) to identify solvent additives that modulate the ambient temperature (T_A) processing window. This approach successfully reveals an additive that enables a record-wide T_A window, from 16 °C to 28 °C, with constantly high power conversion efficiencies (PCEs) exceeding 24%. Mechanistically, in contrast to the previously reported solvent-lead iodide (PbI₂) interaction model, we demonstrate that anchoring formamidinium (FA) cations with the additive to form stable adducts is essential. This interaction effectively suppresses crystallization kinetics, facilitating uniform precursor distribution and high-quality film formation. Importantly, these results challenge the conventional crystallization paradigm for solution-processed perovskites, which emphasizes rapid and complete nucleation during the initial stage of film deposition. Instead, we find that a uniform distribution of precursor ions, even without any nucleation, is sufficient to achieve high-quality perovskite thin films. This work not only demonstrates an effective ML-guided solvent selection strategy but also provides fundamental insight into the primary crystallization requirements for scalable production of high-quality perovskite PV thin films.