Accelerating The Transition from Lab to Fab via High Throughput Automated Synthesis and Characterization of Metal Halide Perovskites

Metal halide perovskites have garnered considerable attention in the field of optoelectronics due to their exceptional properties. However, so far little has been understood based on the fundamental principles for designing the functional perovskites, which is now crucially decelerating the lab-to-fab transformation and realization of the scalable manufacturing of these materials for optoelectronics. In this talk I will discuss the potential of machine learning-driven high throughput automated experiments to expedite the discovery of hybrid perovskites, optimize processing pathways, and enhance the understanding of formation kinetics [1-4]. Notably, the utilization of a high-throughput robotic system to accelerate the exploration of the ligand-assisted reprecipitation (LARP) method for synthesizing perovskite nanocrystals represents a significant contribution to the field [5]. The workflow demonstrated in this study serves as a powerful tool for constructing detailed chemical maps of perovskite nanocrystal synthesis, enabling tailored customization of their functionalities. Additionally, another study showcases how high throughput automated synthesis provides a comprehensive guide for designing optimal precursor stoichiometry to achieve functional quasi-2D perovskite phases in films capable of realizing high-performance optoelectronics [3,4]. I further introduce the concept of co navigation of theory and experiment spaces to accelerate discovery and design of hybrid perovskites. These studies exemplify how a high-throughput automated experimental workflow effectively expedites discoveries and processing optimizations in complex materials systems with multiple functionalities, facilitating their realization in scalable optoelectronic manufacturing processes.<br/><b>References:</b><br/>1. Yang, J., Ahmadi, M. Empowering scientists with data-driven automated experimentation. <i>Nat. Synth</i> (2023). DOI: 10.1038/s44160-023-00337-z<br/>2. Yang J., Kalinin S.V., Cubuk E.D. Ziatdinov M., Ahmadi M. Toward self-organizing low-dimensional organic–inorganic hybrid perovskites: Machine learning-driven co-navigation of chemical and compositional spaces. <i>MRS Bulletin</i> <b>48</b>, 164–172 (2023). DOI: 10.1557/s43577-023-00490-y<br/>3. Yang J, Hidalgo J, Li R, Kalinin SV, Correa-Baena J-P, Ahmadi M. Accelerating materials discovery by high-throughput GIWAXS characterization of quasi-2D formamidinium metal halide perovskites. ChemRxiv (2023). DOI: 10.26434/chemrxiv-2023-x7sfr<br/>4. Yang J, Lawrie BJ, Kalinin SV, Ahmadi M. High-Throughput Automated Exploration of Phase Growth Kinetics in Quasi-2D Formamidinium Metal Halide Perovskites. ChemRxiv (2023). DOI: 10.26434/chemrxiv-2023-zcvl0<br/>5. Sanchez S. L., Tang Y., Hu B., Yang J., Ahmadi M., Understanding the ligand-assisted reprecipitation of CsPbBr₃ nanocrystals via high-throughput robotic synthesis approach. Matter <b>6</b>, 1–19 (2023). DOI: 10.1016/j.matt.2023.05.023