Instead, they assemble into layered separated slabs of [BX6] 4-octahedra and intercalated MA + cations with a structure similar to the one found in ABX3 perovskites. The dielectric mismatch of organic and inorganic components creates a favorable environment for the formation of quantum wells and barriers, generating an excellent perovskite for excitonic phenomena. Like their 3D counterparts, RPPs have easily tunable band gaps, can be solution-processed, and exhibit high device PCE of ~17-18 %. Crystals of RPPs confer a stability increase by virtue of the hydrophobic aliphatic layers of its alternating structure limiting water ingress, and like 3D PSCs, their properties can be enhanced with a range of perovskite additives. For example, polyvinylpolypyrrolidone (PVP), which is widely employed in 3D PSCs, exhibits hydrogen bonding between its carbonyl groups and the protic polar species present in the RPP, leading to the creation of a polymer framework around the crystal structure that furthers enhances stability Additionally, the use of different R + in the crystal lattice relaxes the GTF requirement, resulting in potentially hundreds of thousands of undiscovered, yet highly-performing, compositions. While the RPPs PSC stability is impressive, the current manually fabricated spin-coated devices limit research progress and technology transfer because of lack of cross-laboratory standards and reproducibility. The implementation of fully automated and standardized fabrication and measurement techniques would help to significantly improve the comparability of results between different users and different laboratories. Most highly efficient lead halide PSCs were fabricated via a spin-coating process, yet a number of alternative fabrication techniques have already been employed, primarily: doctor blading, spray, ink-jet, meniscus printing or vacuum deposition. These methods require great skill to control and optimize the film crystallization kinetics, precursor rheology and impact on the film quality. This imposes significant financial and time costs on the optimization of fabrication processes required for production of perovskites with a high degree of reproducibility. Another factor that slows down the progress is the use of one-step-at-atime classical systematic experimentation procedure that has a substantial disadvantage of not considering interactions between input parameters. Remarkably, RPP perovskite precursor solutions at molar concentrations (M) below ~0.7 M crystalize into uniform and highly crystalline films when deposited via a simple drop-casting technique. An interesting method was employed in 2021 by Zuo et al., who used a simple drop casting methodology on mildly preheated substrates for perovskite fabrication. The authors demonstrated that by lowering the concentration of the perovskite precursor solution (< 0.5 M), the solution could spread more evenly across the substrate and form self-assembled, vertically oriented and efficient perovskite films. Extraordinarily, such a simple technique creates new opportunities for rapid compositional screening using drop-cast precursor solutions to form high quality perovskite films. It is known that the reproducibility of PSC fabrication is severely hindered by subtle changes in environmental conditions during deposition, which is likely to impact negatively on the optimization of the fabrication process avoiding this can be accomplished by partial or complete removal of manual handling and human error. In this technique, the mechanism of RPP film formation originates from the precursor solution-substrate surface interactions. Unlike for their 3D counterparts, there are fewer RPP studies focused on crystallization or spread mechanisms (e.g., using additives), controlling the spread and wetting process to ultimately optimize the quality of the resulting RPP film. The drop-casted RPP perovskite film quality depends on a number of factors, such as precursor molarity, substrate temperature and roughness, hydrophilicity or hydrophobicity of the perovskite adjacent charge transporting layer and spread of such precursor, and environmental conditions (pressure, humidity, temperature, atmosphere). Given these factors, we focused on the development of an inexpensive, yet highly controllable and rapidly deployable, technique that expands the mechanism of manual perovskite drop-casting to a rapid combinatorial screening. Herein, we describe the automated, machine learning enhanced combinatorial high-throughput (cHTR) screening deposition strategy that allowed for fabrication of multiple new perovskite film compositions via carefully aliquoted, microfluidically prepared precursors. Our bespoke design of experiment (DoE) allows for fabrication of each PSC layer completely free from human intervention. To further accelerate the parameter optimization process, we developed a state-of-the-art machine learning (ML) protocol from the measurement of electrical and physical properties. This model allows for making predictions on the likely PV properties of RPP films based on a given set of fabrication input parameters. ML algorithms were also used to explore amongst parameters to find the best combinations of permutations in order to develop the device with the highest PV properties. To demonstrate our DoE in this work (Figure ) we selected a small sub-set of compositional and fabrication parameters to be varied and only allow a limited number of discrete values which we chose based on our previous knowledge of RPPs. Within this parameter space we then chose a limited number of permutations, fabricated the RP PSCs, and evaluated their photovoltaic performance. These data sets were then analyzed and employed as data to train a machine learning model. The trained model was then used to gain deeper understanding on the influence of specific parameter values on device performance and to predict a parameter set with optimized performance. For each of the components, there is a number of possible candidates which can be mixed in arbitrary ratios, spanning a de-facto infinite compositional parameter space. In a similar way there is a plethora of processing parameters which need to be defined that will ultimately determine the morphology and quality of the resulting perovskite layer and its performance in a photovoltaic device. This bespoke DoE and the ML model allowed for identification of completely new, improved RPP composition that surpassed the performance of the experimentally chosen champion from the initial subset, yielding to an entirely new champion solar cell with a PCE of ~16.3 % for an active area of 0.1 cm 2 .