A self-driving fluidic lab for data-driven synthesis of lead-free perovskite nanocrystals

Abstract

Copper (Cu)-based metal halide perovskite (MHP) nanocrystals (NCs) have recently gained attention as promising Pb-free and environmentally sustainable alternatives to traditional Pb-based MHPs, offering wide bandgaps, large Stokes shifts, and high emission stability. Despite these advantages, achieving high photoluminescence quantum yields (PLQYs) in Cu-based MHP NCs remains challenging, which impedes their widespread deployment in advanced optoelectronic and energy-related devices. Introducing a metal halide additive in the precursor chemistry can enhance the optical performance of Cu-based MHP NCs, but this approach substantially expands the experimental parameter space, rendering conventional batch-based, trial-and-error methods both time- and resource-intensive. Here, we present a self-driving fluidic lab (SDFL) that combines a modular microfluidic reactor, real-time in situ characterization, and machine-learning-guided decision-making to autonomously explore and optimize high-dimensional Cu-based MHP NC syntheses in the presence of a metal halide additive. Leveraging droplet-based flow chemistry and ensemble neural network-enabled Bayesian optimization, our SDFL rapidly navigates complex precursor formulations and reaction conditions of Cu-based MHP NCs, thus minimizing waste and accelerating discovery. We utilize the SDFL with three distinct precursor chemistries to synthesize Cs₃Cu₂I₅ NCs, with zinc iodide (ZnI₂) serving as the metal halide additive. The high-fidelity data generated in situ allow for the creation of predictive digital twin models that yield mechanistic insights into additive-assisted NC formation. By iteratively refining synthesis parameters within the SDFL, we achieve Cs₃Cu₂I₅ NCs with post-purification PLQYs of approximately 61%, marking a significant improvement over conventional Cu-based MHP NCs. The resulting high-performance, Pb-free NCs underscore the potential of sustainable materials acceleration platforms to speed-up the development of next-generation photonic and energy technologies.