Here, we instead used ML techniques to explore the quantitative relationships between the device fabrication parameters and the photovoltaic performance parameters (PCE, VOC, JSC and FF) of the resulting solar cell devices. To the best of our knowledge, this is the first reported study that employs ML optimization combined with automated fabrication process to design PSCs. We used the MLREM (Multiple Linear Regression with Expectation Maximization) algorithm implemented in the CSIRO-BioModeller® package. In general, ML models require descriptors (mathematical entities that encode physical or chemical properties) to find relationships between descriptors and the target property. In our dataset, the majority of data points (RPP PSCs) had the same chemical components, so molecular descriptors were not required. Thus, we used the list of fabrication parameters for devices (Figure ) as descriptors to build the models. In our DoE we used 1-hot binary descriptors (i.e., 1 if feature is present and 0 if absent) for fabrication parameters such as: 3D-cations, halides, additive options, nnumber of option and annealing temperature.