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Ruddlesden–Popper perovskites (RPPs) are promising materials for optoelectronic devices. While iodide‐based RPPs are well‐studied, the crystallization of mixed‐halide RPPs remains less explored. Understanding the factors affecting their formation and crystallization are vital for optimizing morphology, phase purity, and orientation, which directly impact device performance. Here, we investigate the crystallization and properties of mixed‐halide RPPs (PEA)₂FA_n−1Pb_n(Br_1/3I_2/3)_3n + 1(PEA = C₆H₅(CH₂)₂NH₃⁺and FA = CH(NH₂)₂⁺) (n = 1, 5, 10) using DMSO ((CH₃)₂SO) or NMP (OC₄H₆NCH₃) as cosolvents and MACl (MA = CH₃NH₃⁺) as an additive. For the first time, the presence of planar defects in RPPs is directly observed by in situ grazing‐incidence wide‐angle X‐ray scattering (GIWAXS) and confirmed through the simulation of the patterns that matched the experimental. GIWAXS data also reveals that DMSO promotes higher crystallinity and vertical orientation, while MACl enhances crystal quality but increases halide segregation, shown here by nano X‐ray fluorescence (nano‐XRF) experiments. For low‐n RPPs, orientation is crucial for solar cell efficiency, but its impact decreases with increasing n. Our findings provide insights into optimizing mixed‐halide RPPs, guiding strategies to improve crystallization, phase control, and orientation for better performance not only in solar cells but also in other potential optoelectronic devices. 

Powder X‐ray diffraction (pXRD) experiments are a cornerstone for materials structure characterization. Despite their widespread application, analyzing pXRD diffractograms still presents a significant challenge to automation and a bottleneck in high‐throughput discovery in self‐driving labs. Machine learning promises to resolve this bottleneck by enabling automated powder diffraction analysis. A notable difficulty in applying machine learning to this domain is the lack of sufficiently sized experimental datasets, which has constrained researchers to train primarily on simulated data. However, models trained on simulated pXRD patterns showed limited generalization to experimental patterns, particularly for low‐quality experimental patterns with high noise levels and elevated backgrounds. With the Open Experimental Powder X‐ray Diffraction Database (opXRD), we provide an openly available and easily accessible dataset of labeled and unlabeled experimental powder diffractograms. Labeled opXRD data can be used to evaluate the performance of models on experimental data and unlabeled opXRD data can help improve the performance of models on experimental data, for example, through transfer learning methods. We collected 92,552 diffractograms, 2179 of them labeled, from a wide spectrum of material classes. We hope this ongoing effort can guide machine learning research toward fully automated analysis of pXRD data and thus enable future self‐driving materials labs. 

We present a design strategy for fabricating ultrastable phase-pure films of formamidinium lead iodide (FAPbI₃) by lattice templating using specific two-dimensional (2D) perovskites with FA as the cage cation. When a pure FAPbI₃precursor solution is brought in contact with the 2D perovskite, the black phase forms preferentially at 100°C, much lower than the standard FAPbI₃annealing temperature of 150°C. X-ray diffraction and optical spectroscopy suggest that the resulting FAPbI₃film compresses slightly to acquire the (011) interplanar distances of the 2D perovskite seed. The 2D-templated bulk FAPbI₃films exhibited an efficiency of 24.1% in a p-i-n architecture with 0.5–square centimeter active area and an exceptional durability, retaining 97% of their initial efficiency after 1000 hours under 85°C and maximum power point tracking.