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Abstract Over the past decade, lead halide perovskite (LHP) nanocrystals (NCs) have attracted significant attention due to their tunable optoelectronic properties for next‐generation printed photonic and electronic devices. High‐energy photons in the presence of haloalkanes provide a scalable and sustainable pathway for precise bandgap engineering of LHP NCs via photo‐induced anion exchange reaction (PIAER) facilitated by in situ generated halide anions. However, the mechanisms driving photo‐induced bandgap engineering in LHP NCs remain not fully understood. This study elucidates the underlying PIAER mechanisms of LHP NCs through an advanced microfluidic platform. Additionally, the first instance of a PIAER, transforming CsPbBr₃NCs into high‐performing CsPbI₃NCs, with the assistance of a thiol‐based additive is reported. Utilizing an intensified photo‐flow microreactor accelerates the anion exchange rate 3.5‐fold, reducing material consumption 100‐fold compared to conventional batch processes. It is demonstrated that CsPbBr₃NCs act as photocatalysts, driving oxidative bond cleavage in dichloromethane and promoting the photodissociation of 1‐iodopropane using high‐energy photons. Furthermore, it is demonstrated that a thiol‐based additive plays a dual role: surface passivation, which enhances the photoluminescence quantum yield, and facilitates the PIAER. These findings pave the way for the tailored design of perovskite‐based optoelectronic materials. 

The urgency of finding solutions to global energy, sustainability, and healthcare challenges has motivated rethinking of the conventional chemistry and material science workflows. Self‐driving labs, emerged through integration of disruptive physical and digital technologies, including robotics, additive manufacturing, reaction miniaturization, and artificial intelligence, have the potential to accelerate the pace of materials and molecular discovery by 10–100X. Using autonomous robotic experimentation workflows, self‐driving labs enable access to a larger part of the chemical universe and reduce the time‐to‐solution through an iterative hypothesis formulation, intelligent experiment selection, and automated testing. By providing a data‐centric abstraction to the accelerated discovery cycle, in this perspective article, the required hardware and software technological infrastructure to unlock the true potential of self‐driving labs is discussed. In particular, process intensification as an accelerator mechanism for reaction modules of self‐driving labs and digitalization strategies to further accelerate the discovery cycle in chemical and materials sciences are discussed.