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Identifying the optimal formulation of emerging inorganic lead halide perovskite quantum dots (LHP QDs) with their vast colloidal synthesis universe and multiple synthesis/postsynthesis processing parameters is a challenging undertaking for material‐ and time‐intensive, batch synthesis strategies. Herein, a modular microfluidic synthesis strategy, integrated with an artificial intelligence (AI)‐guided decision‐making agent for intelligent navigation through the complex colloidal synthesis universe of LHP QDs with 10 individually controlled synthesis parameters and an accessible parameter space exceeding 2 × 107, is introduced. Utilizing the developed autonomous microfluidic experimentation strategy within a global learning framework, the optimal formulation of LHP QDs is rapidly identified through a two‐step colloidal synthesis and postsynthesis halide exchange reaction, for 10 different emission colors in less than 40 min per desired peak emission energy. Using two in‐series microfluidic reactors enables continuous bandgap engineering of LHP QDs via in‐line halide exchange reactions without the need for an intermediate washing step. Using an inert gas within a three‐phase flow format enables successful, self‐synchronized continuous delivery of halide salt precursor into moving droplets containing LHP QDs, resulting in accelerated closed‐loop formulation optimization and end‐to‐end continuous manufacturing of LHP QDs with desired optoelectronic properties. An artificial intelligence (AI)‐guided multistep quantum dot (QD) synthesizer robot achieves accelerated bandgap engineering of a high‐priority class of semiconducting materials, lead halide perovskite QDs, via a sequential colloidal synthesis and halide exchange reaction with 10 individually controlled synthesis parameters and an accessible parameter space exceeding 2 × 107 in less than 40 min per target peak emission energy.

Metal halide perovskite (MHP) nanocrystals (NCs) offer extraordinary tunability in their optical properties, yet fully exploiting this potential is challenged by a vast and complex synthesis parameter space. Herein, we introduce Rainbow, a multi-robot self-driving laboratory that integrates automated NC synthesis, real-time characterization, and machine learning (ML)-driven decision-making to efficiently navigate MHP NCs’ mixed-variable high-dimensional landscape. Using parallelized, miniaturized batch reactors, robotic sample handling, and continuous spectroscopic feedback, Rainbow autonomously optimizes MHP NC optical performance—including photoluminescence quantum yield and emission linewidth at a targeted emission energy—through closed-loop experimentation. By systematically exploring varying ligand structures and precursor conditions, Rainbow elucidates critical structure–property relationships and identifies scalable Pareto-optimal formulations for targeted spectral outputs. Rainbow provides a versatile blueprint for accelerated, data-driven discovery and retrosynthesis of high-performance metal halide perovskite nanocrystals, facilitating the on-demand realization of next-generation photonic materials and technologies. The full potential of tunable perovskite nanocrystals is limited by complex synthesis space. Here, authors developed a self-driving lab that autonomously discovers and produces optimal scalable nanocrystals for next-generation photonic technologies.

Lead halide perovskite (LHP) nanocrystals (NCs) are considered an emerging class of advanced functional materials with numerous outstanding optoelectronic characteristics. Despite their success in the field, their precision synthesis and fundamental mechanistic studies remain a challenge. The vast colloidal synthesis and processing parameters of LHP NCs in combination with the batch‐to‐batch and lab‐to‐lab variation problems further complicate their progress. In response, a self‐driving fluidic micro‐processor is presented for accelerated navigation through the complex synthesis and processing parameter space of NCs with multistage chemistries. The capability of the developed autonomous experimentation strategy is demonstrated for a time‐, material‐, and labor‐efficient search through the sequential halide exchange and cation doping reactions of LHP NCs. Next, a machine learning model of the modular fluidic micro‐processors is autonomously built for accelerated fundamental studies of the in‐flow metal cation doping of LHP NCs. The surrogate model of the sequential halide exchange and cation doping reactions of LHP NCs is then utilized for five closed‐loop synthesis campaigns with different target NC doping levels. The precise and intelligent NC synthesis and processing strategy, presented herein, can be further applied toward the autonomous discovery and development of novel impurity‐doped NCs with applications in next‐generation energy technologies. This work features an autonomous robo‐fluidic experimentation strategy for accelerated fundamental and applied studies of colloidal nanocrystals (NCs). The self‐driving fluidic micro‐processors, presented herein, enable rapid navigation through the complex reaction space of emerging advanced functional materials with multistage solution‐processed chemistries.

Lead halide perovskite (LHP) quantum dots (QDs) have emerged as highly promising foundational nanomaterials for advanced energy and optoelectronic applications. This PhD thesis undertakes an extensive fundamental and applied studies of impurity metal cation doping of LHP QDs using modular microfluidic platform integrated with in-situcharacterization probes and assisted with machine learning tools. The PhD thesis presents the development and deployment of a self-driving fluidic lab (SDFL) for accelerated discovery, development, optimization, and fundamental mechanistic studies of metal cation-doped LHP QDs. We further leverage the reconfigurability of SDFLs to enable facile transition from fast-tracked parameter space navigation to on-demand continuous manufacturing of QDs. This PhD thesis encompasses QD synthesis and metal-cation doping chemistries operating at both room and high reaction temperatures, unlocking new possibilities for tailored material properties and applications.The first specific aim of this PhD thesis studies flow chemistry strategies for metal cation doping in LHP QDs. We then utilize the developed flow chemistry approach for funadametnal mechanistic studies of room-temperature manganese (Mn2+) doping of CsPbCl3 QDs by employing an automated modular microfluidic platform. This study is the first report of ultrafast metal-cation doping of LHP QDs at room temperature. Through real-time monitoring of the QD optical properties, we elucidate the kinetics and mechanism of a post-synthetic room-temperature metal cation doping process, enabling precise emission properties tuning of Mn-doped CsPbCl3 QDs through in-flow concentration adjustments of MnCl2 as the Mn2+ ion source. Leveraging the exceptional time resolution of monitoring the LHP QD doping process (as low as 60 ms), enabled by the microfluidic platform, we unveil a two-stage heterogeneous surface doping mechanism facilitated by vacancy-assisted migration of metal cations. Additionally, we utilize the room-temperature metal-cation doping chemistry for ultrafast continuous nanomanufacturing of Mn-doped CsPbCl3QDs. The results of the first specific aim of this PhD thesis enabled the development of an automated and modular flow chemistry platform for reproducible and precise synthesis of colloidal QDs, serving as the core physical infrastructure of SDFLs.The second specific aim of this PhD study, building upon the progress of the first specific aim, investigates integration of machine learning with flow chemistry to build an SDFL for autonomous development of metal-cation-doped LHP QDs via a sequential halide exchange and metal cation doping of LHP QDs using room-temperature chemistries. By integrating the modular flow chemistry platform with a Bayesian framework, we demonstrate constructing a digital twin of the two-stage halide exchange and metal-cation doping of CsPbBr3 QDs for fundamental mechanistic studies. Next, we utilize the digital twin as a surrogate model for ondemand tuning of the LHP QD properties and metal-cation doping level. The developed SDFL accelerates navigation through the multivariate reaction space of the synthesis and metal-cation doping of LHP QDs.In order to further enhance the quality of metal cation-doped LHP QDs, there exists a compelling need to explore and establish flow chemistry synthetic routes operating at high temperatures. Thus, the third specific aim of this PhD thesis focuses on the establishment of an SDFL for one-pot high-temperature metal-cation doping of LHP QDs. In the third specific aim of this PhD thesis, we unveil Smart Dope, that is a self-driving fluidic lab for autonomous hightemperature synthesis, development, and manufacturing of multi cation-doped LHP QDs.

Self‐Driving Fluidic Micro‐Processors In article number 2200017, Milad Abolhasani and co‐workers present a self‐driving lab using artificial intelligence‐guided fluidic blocks for accelerated fundamental and applied studies of emerging clean energy materials. Autonomous doping of metal halide perovskite quantum dots is demonstrated as a material testbed of this self‐driving lab.

Continuous Manufacturing In article number 2000245, Milad Abolhasani and co‐workers present the second generation of “Artificial Chemist”, that is, a modular robo‐fluidic material synthesizer operated by artificial intelligence for data‐driven discovery, formulation optimization, and scalable nanomanufacturing of printable photonic materials with multi‐stage chemistries, including metal halide perovskite quantum dots.