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Abstract The intriguing functionalities of emerging quasi‐2D metal halide perovskites (MHPs) have led to further exploration of this material class for sustainable and scalable optoelectronic applications. However, the chemical complexities in precursors—primarily determined by the 2D:3D compositional ratio—result in uncontrolled phase heterogeneities in these materials, which compromises the optoelectronic performances. Yet, this phenomenon remains poorly understood due to the massive quasi‐2D compositional space. To systematically explore the fundamental principles, herein, a high‐throughput automated synthesis‐characterization workflow is designed and implemented to formamidinium (FA)‐based quasi‐2D MHP system. It is revealed that the stable 3D‐like phases, where the α‐FAPbI₃surface is passivated by 2D spacers, exclusively emerge at the compositional range (35–55% of FAPbI₃), deviating from the stoichiometric considerations. A quantitative crystallographic study via high‐throughput grazing‐incidence wide‐angle X‐ray scattering (GIWAXS) experiments integrated with automated peak analysis function quickly reveals that the 3D‐like phases are vertically aligned, facilitating vertical charge conduction that can be beneficial for optoelectronic applications. Together, this study uncovers the optimal 2D:3D compositional range for complex quasi‐2D MHP systems, realizing promising optoelectronic functionalities. The automated experimental workflow significantly accelerates materials discoveries and processing optimizations that are transferrable to other deposition methods, while providing fundamental insights into complex materials systems. 

Abstract Metal halide perovskites based on formamidinium (FA), or FA‐rich compositions have shown great promise for high‐performance photovoltaics. A deeper understanding of the impact of ambient conditions (e.g., moisture, oxygen, and illumination) on the possible reactions of FA‐based perovskite films and their processing sensitivities has become critical for further advances toward commercialization. Herein, we investigate reactions that take place on the surface of the FA_0.7Cs_0.3, mixed Br/I wide bandgap perovskite thin films in the presence of humid air and ambient illumination. The treatment forms a surface layer containing O, OH, and N‐based anions. We propose the latter originates from formamidine trapped at the perovskite/oxide interface reacting further to cyanide and/or formamidinate—an understudied class of pseudohalides that bind to Pb. Optimized treatment conditions improve photoluminescence quantum yield owing to both reduced surface recombination velocity and increased bulk carrier lifetime. The corresponding perovskite solar cells also exhibit improved performance. Identifying these reactions opens possibilities for better utilizing cyanide and amidinate ligands, species that may be expected during vapor processing of FA‐based perovskites. Our work also provides new insights into the self‐healing or self‐passivating of MA‐free perovskite compositions where FA and iodide damage could be partially offset by advantageous reaction byproducts. image 

Experimental science is enabled by the combination of synthesis, imaging, and functional characterization organized into evolving discovery loop. Synthesis of new material is typically followed by a set of characterization steps aiming to provide feedback for optimization or discover fundamental mechanisms. However, the sequence of synthesis and characterization methods and their interpretation, or research workflow, has traditionally been driven by human intuition and is highly domain specific. Here, we explore concepts of scientific workflows that emerge at the interface between theory, characterization, and imaging. We discuss the criteria by which these workflows can be constructed for special cases of multiresolution structural imaging and functional characterization, as a part of more general material synthesis workflows. Some considerations for theory–experiment workflows are provided. We further pose that the emergence of user facilities and cloud labs disrupts the classical progression from ideation, orchestration, and execution stages of workflow development. To accelerate this transition, we propose the framework for workflow design, including universal hyperlanguages describing laboratory operation, ontological domain matching, reward functions and their integration between domains, and policy development for workflow optimization. These tools will enable knowledge-based workflow optimization; enable lateral instrumental networks, sequential and parallel orchestration of characterization between dissimilar facilities; and empower distributed research. 

Self-driving laboratories (SDLs) are the future for scientific discovery in a world growing with artificial intelligence. The interaction between scientists and automated instrumentation are leading conversations about the impact of SDLs on research.