1 Machine Learning Approach for Metal Oxide based Polymer Composites as Charge Selective Layers in Perovskite Solar Cells Murat Onur Yildirim a* , Elif Ceren Gok a* , Naveen Harindu Hemasiri b , Esin Eren d,e* , Samrana Kazim b,c , Aysegul Uygun Oksuz e* , Shahzada Ahmad b,c* a Department of Industrial Engineering, Engineering Faculty, Suleyman Demirel University, 32260, Isparta, Turkey b BCMaterials, Basque Center for Materials, Applications and Nanostructures, UPV/EHU Science Park, 48940, Leioa, Spain c IKERBASQUE, Basque Foundation for Science, Bilbao, 48009, Spain d Department of Energy Technologies, Innovative Technologies Application and Research Center, Suleyman Demirel University, 32260, Isparta, Turkey. e Department of Chemistry, Faculty of Arts and Science, Suleyman Demirel University, 32260 Isparta, Turkey Supporting information for this article is given via a link at the end of the document Abstract: A library of metal oxide-conjugated polymer composites was synthesized, encompassing of WO3-polyaniline (PANI), WO3- poly(N-methylaniline) (PMANI), WO3-poly(2-fluoroaniline) (PFANI), WO3-polythiophene (PTh), WO3-polyfuran (PFu) and WO3-poly(3,4- ethylenedioxythiophene) (PEDOT). These composites were probed as hole selective layers for perovskite solar cells (PSCs) fabrication. We adopted machine learning approaches to predict and compare PSCs performances with the developed WO3 and its composites. The experimental and theoretical results are coherent, when the electro- optical properties of PSC were computed. Notably, for the evaluation of PSCs performance, decision tree model is the ideal for WO3- PEDOT composite, while random forest model was found to be suitable for WO3-PMANI, WO3-PFANI, WO3-PFu. While in the case for WO3, WO3-PANI and WO3-PTh, K Nearest Neighbors model was appropriate. Machine learning models can be a pioneering prediction models for the PSCs performance and its validation. Introduction Perovskite solar cells (PSCs) are being intensively research due to broad light absorption (300-800 nm), high absorption coefficient, long carrier diffusion length, high charge carrier mobility and tuneable bandgap. [1-2] Organic–inorganic halide PSCs have gained significant attraction due to simple fabrication process and high-power conversion efficiency (PCE). [3] The Perovskite is represented by a typical formula of ABX3, here A is an organic cation such as methylammonium (MA), formamidinium (FA), etc.; B is a metal (typically Pb); and X is a halogen anion (I, Br, Cl, or a mixture of these). [3] MA based PSCs lacks behind desired characteristics of thermal stability, moisture-induced degradation, and hysteretic I−V behaviour, which limits its applications. [3] Mixed cation and anion based perovskites showed advantageous properties [3-5] and mixed cation of formamidinium/methylammonium (FAMA) gave enhanced performance due to an intense light absorption, stability and reduced J−V hysteresis. [3] Further, the incorporation of cesium (Cs) can also efficaciously decreases the crystallization temperature during the annealing process and induce reliability. [3, 5] Depending on the light incidence, the architect of PSCs can be either n-i-p or p-i-n type, where n- and p-type are electron and hole selective materials respectively, and i denotes to the light harvesting layer. [6] PSCs with p-i-n planar architecture display negligible hysteresis, solution processability at low temperature, and the potential for scale up using a continual coating method. [7] Fullerenes type acceptor, especially [6,6]-phenyl-C(61)-butyric acid methyl ester (PCBM), are typically used as n-type charge transport layer [7] , while poly(3,4- ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) as a transparent hole transport material in lieu of Spiro-OMeTAD, was employed. [8] However, PEDOT:PSS possesses undesirable features such as hygroscopic nature, inferior thermal stability and inability to block electrons [8] , thus an effective hole transport layer (HTL) is paramount. Cogal et al. reported graphene-based polymer composites as HTL. [9] Recently, transition metal oxides such as WO3, owing to its high work functions, high carrier mobility, and excellent thermal stability are being used as HTLs. [8] Comprehensive study on metal oxide based HTLs suggests that the hybrid organic-inorganic composite can be promising candidates as injecting carriers from perovskite absorber to electrode. [10-12] Machine learning (ML) is a set of methods that can acquire the data pattern without explicit programming and predict the imminent data with uncovered patterns. [13] It will replace the traditional trial-error method which demands longer time and resources to predict the performance and reliability of PSCs (stability, PCE, fabrication techniques and material synthesis). [14- 16] ML based data-driven is necessary to be coupled with experimental data for the modelling. [17] An extensive amount of data is not required and computing time is fast as compared to other complicated models. [18] By employing ML technique in solar cells, material properties, optimized device architects and fabrication processes can be predicted, and data reconstruction is attracting significant interests for research and development. [18] Reports dealing with PSCs through ML approach are in scarce [19] , the majority of prediction of materials entails, electro-optical features, J-V performance, which are associated with dye- sensitized and organic solar cells. [18] Thermodynamic stability of 20,000 randomly selected materials using seven different ML methods was predicted. [20] Random forest (RF) model predicts band gap of perovskites by employing 18 descriptors and 10 stable perovskites and band gaps were elucidated. [21] Similarly, discovery of materials with an optimal bandgap for single junction was predicted. [22] 300 octahedral oxyhalides with geometric and electronic data were used to sequence ML model and the band gap predictions were made on 5000 oxyhalides test data. [23] ML methods were used to develop model that contains 333 data points from 2000 scientific articles to guide the designing of new perovskite to predict model performance. [16] Using alternating conditional expectations – ML approach, nonlinear mapping between band gap and properties of constituent elements were studied. [24] Methodology was presented to predict bandgap of undiscovered 5158 hybrid perovskites for PSCs, for this, six different ML regression algorithms was constructed and implemented.