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Abstract Quasi‐2D metal halide perovskites (MHPs) are an emerging material platform for sustainable functional optoelectronics, but the uncontrollable, broad phase distribution remains a critical challenge for applications. Nevertheless, the basic principles for controlling phases in quasi‐2D MHPs remain poorly understood, due to the rapid crystallization kinetics during the conventional thin‐film fabrication process. Herein, a high‐throughput automated synthesis‐characterization‐analysis workflow is implemented to accelerate material exploration in formamidinium (FA)‐based quasi‐2D MHP compositional space, revealing the early‐stage phase growth behaviors fundamentally determining the phase distributions. Upon comprehensive exploration with varying synthesis conditions including 2D:3D composition ratios, antisolvent injection rates, and temperatures in an automated synthesis‐characterization platform, it is observed that the prominent
n = 2 2D phase restricts the growth kinetics of 3D‐like phases—α‐FAPbI3MHPs with spacer‐coordinated surface—across the MHP compositions. Thermal annealing is a critical step for proper phase growth, although it can lead to the emergence of unwanted local PbI2crystallites. Additionally, fundamental insights into the precursor chemistry associated with spacer‐solvent interaction determining the quasi‐2D MHP morphologies and microstructures are demonstrated. The high‐throughput study provides comprehensive insights into the fundamental principles in quasi‐2D MHP phase control, enabling new control of the functionalities in complex materials systems for sustainable device applications. -
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In the last several years, laboratory automation and high‐throughput synthesis and characterization have come to the forefront of the research community. The large datasets require suitable machine learning techniques to analyze the data effectively and extract the properties of the system. Herein, the binary library of metal halide perovskite (MHP) microcrystals, MA
x FA1−x PbI3−x Brx , is explored via low‐dimensional latent representations of composition‐ and time‐dependent photoluminescence (PL) spectra. The variational autoencoder (VAE) approach is used to discover the latent factors of variability in the system. The variability of the PL is predominantly controlled by compositional dependence of the bandgap. At the same time, secondary factor of variability includes the phase separation associated with the formation of the double peaks. To overcome the interpretability limitations of standard VAEs, the workflow based on the translationally invariant variational (tVAEs) and conditional autoencoders (cVAEs) is introduced. tVAE discovers known factors of variation within the data, for example, the (unknown) shift of the peak due to the bandgap variation. Conversely, cVAEs impose known factor of variation, in this case anticipated bandgap. Jointly, the tVAE and cVAE allow to disentangle the underlying mechanisms present within the data that bring a deeper meaning and understanding within MHP systems.
