Lead-Free Halide Perovskites for Photocatalysis via High-Throughput Exploration

This Perspective navigates the transformative synergy between machine learning (ML) techniques and high-throughput (HT) methodologies in the realm of photocatalysis, aiming to overcome the inefficiencies and drawbacks associated with existing photocatalysts. Pb-free hybrid perovskite (HP) nanocrystals (NCs) emerge as promising candidates, offering distinctive physicochemical and optical attributes in addition to nontoxicity. The integration of HT automated methods accelerates the synthesis and characterization of novel Pb-free HP materials while also addressing challenges in obtaining large, high-quality data sets for training ML models. The proposed multidisciplinary approach, combining experimental and computational simulations, aims to unravel the complexities of photocatalytic systems, fostering the development of innovative strategies for materials development. The convergence of experimental techniques, computational simulations, and ML is poised to revolutionize photocatalysis (PC), propelling the field into an era of unprecedented discovery and innovation.