AI-accelerated scalable synthesis of nanocrystals for cost-effective photovoltaics

Metal halide perovskite nanocrystals (PNCs) are promising candidates for next-generation optoelectronics. However, their commercial application necessitates low-cost synthesis and processing methods that surpass conventional lab-scale techniques. To address this, we report a ligand triggered room-temperature injection (LTRI) method that initiates nanocrystal nucleation by tuning precursor reactivity with addition of short-chain octanoic acid (OcA). Subsequently, integrating automation synthesis and machine learning, we further develop an efficient AI-accelerated framework towards fast and target synthesis of the FAPbI₃ (FA: formamidinium) PNCs. Leveraging machine learning modeling, we identify that the critical size and distribution of FAPbI₃ PNCs can be effectively optimized by tuning the feed ratio of OcA to the precursor formamidinium acetate. Furthermore, our LTRI method is fully compatible with flow-synthesis system for scale-up production, enabling a substantial reduction in production cost to US$2.16 g^-1, significantly lower than the widely adopted hot-injection method (US$118.81 g^-1). For practical application, we achieve a power conversion efficiency of 19.37% in photovoltaic applications with significant enhancement in device stability, matching the highest reported results for nanocrystal-based solar cells.