Sanjit Ghose1,Matthew Greenberg2,ChengHung Lin1,Ai Kagawa1
Brookhaven National Laboratory1,Bard College2
Sanjit Ghose1,Matthew Greenberg2,ChengHung Lin1,Ai Kagawa1
Brookhaven National Laboratory1,Bard College2
Metal halide perovskites quantum dots (QDs) have recently attracted attention for use in a wide variety of optoelectronic applications, including solar cells and light emitting diodes, due to their ease of synthesis, strong light absorption, and chemical tunability. The chemical synthesis of these materials enables precise control over the size, shape, and composition. Despite substantial progress in the synthesis of QDs over the past three decades, issues with scalability and batch-to-batch variation persist. These issues are a symptom of the complexity of crystal nucleation, limited understanding of the QDs formation mechanisms, and poor control over the rapid kinetics of formation. To address these issues, we developed a continuous flow reactor with automated input parameter control during the reaction process. The solution is monitored <i>in situ</i> to measure the reactants and intermediates along each stage of the nucleation and growth processes. The ability to rapidly canvas the input parameter space and collect <i>in situ</i> Synchrotron X-ray and photophysical data generates large data sets to identify the effects of reaction conditions on reaction outcomes. The data was analyzed to obtain corresponding time resolved structure, morphological information, and photophysical properties. Subsequently, using Artificial Iinteligence (AI) & Machine Learning(ML) algorithms to understand the underpinning mechanisms, we identify variables that control the outcome of the colloidal synthesis, and achieve the targeted structure of the PQDs. In this case, we studied sub-sec snap shots of nanocrystal pre-nucleation, nucleation, and growth processes of room temperature flow synthesis of CsPbX<sub>3 </sub>(X= Br, Cl, I) at NSLS-II synchrotron X-ray beamlines. This approach enables precise control over the synthesis outcome and enhances the speed of targeted Quantum materials discovery.