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Abstract Amphiphilic copolymers (AP) represent a class of novel antibiofouling materials whose chemistry and composition can be tuned to optimize their performance. However, the enormous chemistry‐composition design space associated with AP makes their performance optimization laborious; it is not experimentally feasible to assess and validate all possible AP compositions even with the use of rapid screening methodologies. To address this constraint, a robust model development paradigm is reported, yielding a versatile machine learning approach that accurately predicts biofilm formation by Pseudomonas aeruginosa on a library of AP. The model excels in extracting underlying patterns in a “pooled” dataset from various experimental sources, thereby expanding the design space accessible to the model to a much larger selection of AP chemistries and compositions. The model is used to screen virtual libraries of AP for identification of best‐performing candidates for experimental validation. Initiated chemical vapor deposition is used for the precision synthesis of the model‐selected AP chemistries and compositions for validation at solid–liquid interface (often used in conventional antifouling studies) as well as the air–liquid–solid triple interface. Despite the vastly different growth conditions, the model successfully identifies the best‐performing AP for biofilm inhibition at the triple interface. 

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.