Synthetic dataset to study the performance of perovskite solar cell simulations

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The dataset consists of 18.570 simulated devices generated from four baseline device architectures and their respective photovoltaic performance values. The data set was generated through numerical simulations, and the evaluation of the electrical performance of the device was carried out by studying current density-voltage (J-V) curves under standard illumination conditions, temperature, and maximum applied voltage as working conditions, which were not modified. The dataset can be used to train different machine learning (ML) models using supervised methods or unsupervised techniques such as clustering or dimensionality reduction, which facilitate the identification of patterns or relationships between parameters. Thus, it can be useful in reverse design strategies to determine optimal configurations based on defined objectives. This work contributes to the development of PSC by providing a broad dataset for further analysis and optimization." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-961/v1", "name": "Synthetic dataset to study the performance of perovskite solar cell..." } } ] } Home Browse Synthetic dataset to study the performance of perovskite solar cell... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Velez-Galvis Y, Gonzalez-Valencia E, Sepulveda-Sepulveda A and Gomez-Cardona N. Synthetic dataset to study the performance of perovskite solar cell simulations [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :961 ( https://doi.org/10.12688/f1000research.168996.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Data Note Synthetic dataset to study the performance of perovskite solar cell simulations [version 1; peer review: 1 approved with reservations] Yeraldin Velez-Galvis https://orcid.org/0009-0009-5180-8839 1 , Esteban Gonzalez-Valencia https://orcid.org/0000-0003-3707-5738 1 , Alexander Sepulveda-Sepulveda 2 , Nelson Gomez-Cardona 1 Yeraldin Velez-Galvis https://orcid.org/0009-0009-5180-8839 1 , Esteban Gonzalez-Valencia https://orcid.org/0000-0003-3707-5738 1 , Alexander Sepulveda-Sepulveda 2 , Nelson Gomez-Cardona 1 PUBLISHED 22 Sep 2025 Author details Author details 1 Departamento de Electrónica y Telecomunicaciones, Instituto Tecnológico Metropolitano, Medellín, Antioquia, 050034, Colombia 2 Escuela de Ingenierías Eléctrica, Electrónica y de Telecomunicaciones, Universidad Industrial de Santander, Bucaramanga, Santander, 680002, Colombia Yeraldin Velez-Galvis Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Visualization, Writing – Original Draft Preparation Esteban Gonzalez-Valencia Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing – Original Draft Preparation Alexander Sepulveda-Sepulveda Roles: Conceptualization, Formal Analysis, Funding Acquisition, Project Administration, Supervision, Validation, Writing – Review & Editing Nelson Gomez-Cardona Roles: Conceptualization, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Visualization, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Energy gateway. This article is included in the Artificial Intelligence and Machine Learning gateway. This article is included in the Solar Fuels and Storage Technologies collection. This article is included in the Perovskite Solar Cells collection. Abstract This paper presents a synthetic dataset to study the performance of perovskite solar cells (PSC) simulations using the simulation tool SCAPS-1D. The dataset consists of 18.570 simulated devices generated from four baseline device architectures and their respective photovoltaic performance values. The data set was generated through numerical simulations, and the evaluation of the electrical performance of the device was carried out by studying current density-voltage (J-V) curves under standard illumination conditions, temperature, and maximum applied voltage as working conditions, which were not modified. The dataset can be used to train different machine learning (ML) models using supervised methods or unsupervised techniques such as clustering or dimensionality reduction, which facilitate the identification of patterns or relationships between parameters. Thus, it can be useful in reverse design strategies to determine optimal configurations based on defined objectives. This work contributes to the development of PSC by providing a broad dataset for further analysis and optimization. READ ALL READ LESS Keywords Pervoskite solar cell, SCAPS-1D, dataset, photovoltaic technology, numerical simulation, machine learning. Corresponding Author(s) Yeraldin Velez-Galvis ( [email protected] ) Close Corresponding author: Yeraldin Velez-Galvis Competing interests: No competing interests were disclosed. Grant information: This work was funded by MinCiencias-Colombia, with resources administered by ICETEX-Colombia (project code 2022-0724) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Velez-Galvis Y et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Velez-Galvis Y, Gonzalez-Valencia E, Sepulveda-Sepulveda A and Gomez-Cardona N. Synthetic dataset to study the performance of perovskite solar cell simulations [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :961 ( https://doi.org/10.12688/f1000research.168996.1 ) First published: 22 Sep 2025, 14 :961 ( https://doi.org/10.12688/f1000research.168996.1 ) Latest published: 22 Oct 2025, 14 :961 ( https://doi.org/10.12688/f1000research.168996.2 )  There is a newer version of this article available. Suppress this message for one day. 1. Introduction Perovskite solar cells (PSC) have emerged as one of the most promising technologies in the field of photovoltaic energy because of their high absorption coefficient, low manufacturing costs and great versatility in device design. 1 – 3 However, the optimization of these solar cells involves a complex interaction between optical, electric, and structural properties of multiple functional layers. 4 – 6 The experimental exploration of this design space is expensive and time-consuming, which has driven the increasing use of computational simulations as a complementary tool to understand and predict the performance of these devices. 7 – 9 Multiple configurations have already been studied under standardized and comparable conditions using different simulation tools, 10 – 18 which are essential to validate the implementation of new numerical analysis. The use of the software SCAPS-1D (Solar Cell Capacitance Simulator) has become popular because it is freely available and its versatility for modeling thin-film heterojunction solar cells. 19 SCAPS-1D solves Poisson and continuity equations to calculate the photovoltaic performance, considering charge generation, recombination mechanisms, and transport through multilayer structures. 20 SCAPS-1D results allow us to understand how the photoelectric properties of PCS affect its performance, 21 representing a useful tool for designing PSC. Additionally, the integration of machine learning (ML) with device simulations has been proposed, showing promise for accelerating materials development and device optimization. 22 – 25 For example, Odabaşı and Yıldırım used data mining and decision trees to analyze the impact of deposition methods on cell efficiency, concluding that the quality of the data input into the algorithm is key to obtaining accurate results. 26 In 2022, Yan et al. applied supervised models such as XGBoost and random forest with GridSearchCV to predict bandgap, Jsc, and Voc values, obtaining a 2% margin of error compared to experimental measurements. 27 In 2023, a study was published that combined SCAPS-1D with ML tools (RandomSearchCV and GridSearchCV) to predict the best material combination for perovskite and HTL, achieving an efficiency of 23.9% with 75% accuracy. 24 In 2023, Lu et al. used supervised and unsupervised algorithms to predict cell performance based on experimental data, concluding that A cations increase the device’s energy efficiency. 28 Recently, in 2024 Shrivastav et al. integrated SCAPS-1D with ML models to analyze six cesium-based perovskite materials. They used 2160 simulations varying thickness, doping, and defect density, achieving a maximum efficiency of 14% with CsPbI 3 and a coefficient of determination R 2 of 99.99% with XGBoost. Additionally, they used SHAP analysis and revealed that the absorber layer material and its thickness are the most influential factors on efficiency. 7 In this context, the development of synthetic databases acquires strategic relevance. These databases not only allow the systematization of knowledge about the relationships between material parameters and photovoltaic performance but also allow the training of ML models, 25 , 27 , 29 , 30 the application of optimization techniques, 31 and the reverse design of solar cells. 28 This work presents a structured database composed of 18.570 simulations of PSC generated with SCAPS-1D, a simulation tool freely provided for academic use by the University of Gent in Belgium. 32 Four structures were analyzed, considering systematic variations of the active material and geometric parameters of the cell, where the materials most widely used in the literature were included to ensure the practical relevance of the dataset. Each entry in the dataset includes the input parameters that describe optical, electrical, and physical properties of the solar cell, as well as the electrical performance results in terms of open circuit voltage (Voc), short circuit current density (Jsc), fill factor (FF) and power conversion efficiency (PCE). This database represents a valuable tool for the scientific community, allowing researchers to evaluate the individual or combined impact of key parameters on device efficiency, train ML models for performance predictions for the sake of obtaining surrogate models. These models can facilitate the integration and fusion of domain knowledge into more complex machine learning models that include synthesis conditions for solar cells. They would also allow the application of multi-objective optimization techniques to improve solar cell efficiency. In this way, this work aims to contribute to the accelerated advancement of the design of photovoltaic devices through reproducible and accessible computational approaches. 2. Methods 2.1 Device design The structure shown in Figure 1 corresponds to a nip-type PSC 33 with five layers, which is the configuration studied in this work. The first and last layers are the electrical contacts, while the internal layers are responsible for the device’s energy conversion. For the top contact, fluorine-doped tin oxide (FTO) was used since it is ideal to function as a transparent electrode. Titanium oxide (TiO 2 ) and tin oxide (SnO 2 ) were used for the electron transport layer (ETL) due to their electronic properties and proven use in the scientific literature. 17 , 34 – 38 For the perovskite absorber layer, methylammonium lead iodide (MAPbI 3 ), methylammonium tin iodide (MASnI 3 ) and formamidinium lead iodide (FAPbI 3 ) were used because these materials have the highest reported energy efficiencies and have been extensively studied by the scientific community. 10 , 25 , 39 – 41 For the hole transport layer (HTL), Spiro-OMeTAD and copper(I) thiocyanate (CuSCN) were used because configurations with these materials have demonstrated remarkable performance in hole mobility and effective energy alignment. 41 – 43 Finally, the last layer is generally made of gold (Au) since it has high electrical conductivity. 43 These materials were chosen for their optoelectronic properties, energy compatibility, and the high performance demonstrated in experimental and simulated studies available in the literature. 11 , 20 , 21 , 35 , 41 , 43 – 47 To convert solar energy into electrical energy, the PSC absorbs photons from solar radiation in the perovskite layer, generating electron-hole pairs, which are separated and transported by the ETL layer, which extracts the electrons, while the HTL layer collects the holes. The top electrode, usually made of a transparent conductive oxide like FTO, allows the entry of light and the collection of carriers, while the bottom metallic electrode completes the circuit, allowing the flow of external current under load conditions. Figure 1. Perovskite solar cell structure. 2.2 Numerical modelling of PSC SCAPS-1D analyzes the electrical response of a PSC solving a coupled set of differential equations that include the Poisson equation (1) , the continuity equations for electrons (2) and holes (3), and the performance metrics equations (4)-(7) . The Poisson equation is presented below: (1) ∂ ∂ x ( ε 0 ε r ∂ ψ ∂ x ) = − q ( p − n + N D + − N A − + ρ def q ) where ψ is the electrostatic potential, q is the elementary charge, ε 0 and ε r are the vacuum and the relative permittivity, p and n are hole and electron concentrations, N D + and N A − are charge impurities of donor and acceptor type, and ρ def is the defect density. The continuity equations for electrons (2) and holes (3) are given by: (2) − ∂ J n ∂ x − U n + G = ∂ n ∂ t , (3) − ∂ J p ∂ x − U p + G = ∂ p ∂ t . where J n and J p are the electron and hole current densities, G is the electron-hole generation rate, and U n and U p are the electron and hole recombination rates. To calculate the performance metrics Voc (4), Jsc (5), FF (6), and PCE (7), SCAPS-1D uses the equations presented below: (4) J SC = ∫ 0 λ max q ϕ ( λ ) EQE ( λ ) dλ (5) V OC = kT q ln ( J SC J 0 + 1 ) , (6) FF = V mp ⋅ J mp V OC ⋅ J SC , (7) PCE = V OC ⋅ J SC ⋅ FF P in where λ is the wavelength, ϕ ( λ ) is the incident solar spectrum and EQE ( λ ) is the external quantum efficiency, k is the Boltzmann constant, T is the absolute temperature, J 0 is the reverse saturation current, V mp and J mp are the voltage and current at the point of maximum power, and P in is the incident power. 2.3 Simulation data generation The data set was generated through numerical simulation using the freely available software SCAPS-1D version 3.3.09 and the evaluation of the electrical performance of the device was carried out by studying J-V curves under the standard working conditions of AM1.5G illumination (1000 W/m 2 ), temperature of 300 K and maximum applied voltage of 1.2 V. 48 From these curves, the main electrical parameters that characterize the performance of the system were determined, including the Voc, Jsc, FF and PCE. These parameters were obtained directly from the software after simulating the optoelectronic behavior of the device, allowing a precise evaluation of the expected performance and allowing comparative analysis in terms of efficiency, stability, and robustness against variations in the materials, properties or thickness of the studied layers. The selection of parametric variation was based on their direct influence on the physical and electrical device performance. The thickness of the perovskite layer (T_PVK) must be adjusted to absorb the largest amount of photons, maximizing Jsc without exceeding the carrier diffusion length, since excessive thicknesses increase recombination losses and degrade Voc and FF. 49 Similarly, the thicknesses of the transport layers (T_ETL and T_HTL) must be optimized to ensure efficient electron and hole transport with low recombination and series resistance. 50 , 51 Additionally, the properties of perovskite have a direct influence on the performance of the cell; the bandgap (EG_PVK) establishes the balance between the current density and voltage, based on the Shockley-Queisser limit 52 ; the dielectric permittivity (ER_PVK) influences exciton dissociation 53 , 54 ; the acceptor density (NA_PVK) models the internal electric field, essential for charge separation and a high Voc 55 ; and the defect density (NT_PVK) represents the main pathway for non-radiative recombination loss and limiting the carrier lifetime. 56 The variation ranges are specified in Table 1 and parameter combinations leading to convergence errors in the software were discarded, as these typically arose from physically realistic ranges, disrupting the solution of Poisson’s equation, continuity equations, or boundary conditions. Table 1. Variation ranges of geometric and physical parameters used for simulation. Layer Parameter Range Units ETL T_ETL 0.02 – 0.2 μ m HTL T_HTL 0.1 – 0.7 μ m Perovskite T_PVK 0.1 – 1.7 μ m E g 1.1 – 1.9 eV ε r 8 – 20 – N A 1 × 10 13 – 1 × 10 17 cm −3 N t 4 × 10 13 – 4 × 10 15 cm −3 2.4 Data validation The accuracy of the simulation methodology was validated by successfully reproducing the results reported by research articles as shown in Table 2 . For this purpose, over 50 recently published scientific articles were collected that included most of the parameters to simulate a nip-type PSC in SCAPS-1D and also reported the values of Voc, Jsc, FF, and PCE. After a systematic review, to avoid unrealistic results, articles reporting energy efficiencies above the Shockley-Queisser limit 57 for cells based on MAPbI 3 , MASnI 3 and FAPbI 3 were excluded, as, according to experimental validations, 58 the maximum efficiency achieved for these materials does not exceed 22.2 % , 14.35 % and 24.66 % , respectively. It is important to note that, in the simulations of PSC, variable physical (surface roughness, grain size, and orientation), chemical (temperature and drying time, solvent and antisolvent engineering, and additives) and environmental factors (temperature variations, cloud cover, irradiance, etc.) are not incorporated, which can affect the actual performance of the cells. Therefore, it is expected that the values obtained through simulation will be higher than those observed experimentally maintaining consistency with realistic values. 34 , 58 Table 2. Comparison between reported and simulated performance results for PSC. Reference Reported Simulated V OC (V) J SC (mA/cm 2 ) FF (%) PCE (%) V OC (V) J SC (mA/cm 2 ) FF (%) PCE (%) 59 1.04 30.5 82.69 26.95 1.02 29.8 78.28 26.12 60 0.98 18.6 82.50 13.40 0.96 18.2 79.97 13.07 61 1.02 22.7 62.67 21.42 1.01 22.4 65.92 21.26 62 0.91 24.1 54.19 16.08 0.93 24.3 78.98 16.49 63 0.87 24.9 85.80 15.50 0.85 24.4 81.26 15.14 A comparison of values obtained through simulation and values reported in the literature for Voc, Jsc, FF, and PCE is shown in Table 2 . There are slight variations in the obtained values compared to the reported ones, which can be attributed to multiple causes, as very few authors report in detail all the simulation conditions or all the parameters used. Some studies include models for recombination, absorption or defects without specifying numerical values (defect density, defect type, or recombination coefficients); therefore, the exact replication of the simulation conditions is limited. This is crucial for a simulation since it considers recombination phenomena, losses due to defects of the layers or interfaces derived from manufacturing processes or impurities in the materials that compose the cell, which can significantly affect the performance of the system. Although the variations in the reported parameters prevent an identical replication, the obtained results show consistency with the published data, supporting the robustness of the methodology. 3. Data description The dataset comprises 18.570 simulated PSC generated from four device architectures: TiO 2 /MAPbI 3 /CuSCN, 60 TiO 2 /MASnI 3 /Spiro-OMeTAD, 59 SnO 2 /FAPbI 3 /Spiro-OMeTAD, 64 and TiO 2 /MAPbI 3 /Spiro-OMeTAD. 65 The performance results of the cells were obtained by using the SCAPS-1D option “Batch set-up”, which allows carrying out a parametric study of PSC in specific value ranges and obtaining the results associated with all the combinations; the ranges specified in Table 1 were used, and only combinations that produced convergence errors were discarded. The dataset includes, for each record, nineteen PSC features (those could be taken as inputs or “X” values in case ML application is implemented) and four associated results, such as Voc, Jsc, FF, and PCE (that could be used as outputs or “y” values). The description of each convention name in the column is as follows: Material (M) : • Column A: Material of the ETL layer (M_ETL). • Column B: Material of the perovskite absorber layer (M_PVK). • Column C: Material of the HTL layer (M_HTL). Ranging parameters : The next columns correspond to parameters that were varied as shown in Table 1 : • Column D: Thickness of ETL layer in μ m ( T _ ETL ). • Column I: Thickness of absorber layer in μ m ( T _ PVK ). • Column J: Bandgap of absorber layer in eV ( EG _ PVK ). • Column K: Dielectric permittivity of absorber layer ( ER _ PVK ). • Column L: Shallow acceptor density of absorber layer in cm − 3 ( NA _ PVK ). • Column N: Defect density of absorber layer in cm − 3 ( NT _ PVK ). • Column O: Thickness of HTL layer in μ m ( T _ HTL ). Constant value parameters : The next columns have constant values for the specific parameters. It is important to include them to validate the results presented in this work. • Column E: Bandgap of ETL layer in eV ( EG _ ETL ). • Column F: Dielectric permittivity value of ETL layer ( ER _ ETL ). • Column G: Shallow donor density of ETL layer in cm − 3 ( ND _ ETL ). • Column H: Defect density value of ETL layer in cm − 3 ( NT _ ETL ). • Column M: Shallow donor density value of absorber layer in cm − 3 ( ND _ PVK ). • Column P: Bandgap of HTL layer in eV ( EG _ HTL ). • Column Q: Dielectric permittivity of HTL layer ( ER _ HTL ). • Column R: Shallow donor density of HTL layer in cm − 3 ( ND _ HTL ). • Column S: Defect density of HTL layer in cm − 3 ( NT _ HTL ). Performance metrics : • Column T: Open circuit voltage in V ( VOC ). • Column U: Short circuit current density in mA / cm 2 ( JSC ) • Column V: Fill factor in percentage ( FF ). • Column W: Power conversion efficiency in percentage ( PCE ). Basic descriptive statistics were conducted for the dataset, generating the data distribution for the performance metrics (Voc, Jsc, FF, and PCE). Figure 2 a) presents the data distribution for Voc, which shows a main peak in the multimodal distribution with a mean of 0.98 V and a median of 1.00 V, with a standard deviation of 0.14 V and an interquartile range (IQR) of 0.90 V–1.10 V. A smaller number of parametric combinations are observed for values below 0.7 V, which can be attributed to increased recombination due to the geometric configuration of the device or improper band alignment. 57 , 66 The data distribution of Jsc presented in Figure 2 b), shows a multimodal distribution with four marked peaks around 16 mA / cm 2 , 21 mA / cm 2 , 27 mA / cm 2 , and 34 mA / cm 2 , with a mean of 20.7 mA / cm 2 ; median of 20.2 mA / cm 2 ; and standard deviation of 9.4 mA / cm 2 . The peaks suggest subsets defined by discrete thicknesses of the perovskite or by steps in the optical absorption imposed during the parametric sweep. Physically, current densities below 10 mA / cm 2 are associated with thin films or large bandgaps, while values above 30 mA / cm 2 are associated with sufficiently thick layers with low defect density, where carrier absorption and collection are maximized. 67 , 68 Figure 2 c) presents the data distribution of the FF, where a noticeable peak is observed in values close to 79%, with a mean of 65.2%, a median of 69.2%, and a standard deviation of 15.8%. This demonstrates a high dispersion in the data, due to a considerable amount of data being located at values below 60%, which significantly affects the FF distribution for the dataset and shows that some configurations can generate losses, either due to recombination or cell defects. 69 Finally, Figure 2 d) presents the data distribution of the PCF, which shows a peak around 9% with a mean of 12.8%, a median of 12.1%, and a standard deviation of 6.26%, exhibiting a broad and slightly bimodal shape: one cluster between 5% and 15% associated with devices with one or two suboptimal parameters (e.g., moderate Jsc and acceptable FF) and another peak between 18–23% that asociated with Voc and FF values. The decreasing trend of 25% reflects the limit imposed by maximum absorption and residual non-radiative losses, consistent with the Shockley-Queisser model. 57 , 70 In summary, the dispersion demonstrates how the joint variation of thickness, bandgap, and defects controls the efficiency, reproducing the range of values reported experimentally. Figure 2. Data distribution of the performance metrics: a) Voc, b) Jsc, c) FF, and d) PCE. The dataset was stored in OSF HOME, an open-source platform for managing and sharing research data. The project, titled “Synthetic dataset to study the performance of perovskite solar cell simulations”, with DOI: 10.17605/OSF.IO/ZX4AJ includes the file “Synthetic dataset to study the performance of perovskite solar cell simulations.xlsx”, which contains all simulated device configurations and their corresponding performance metrics. 4. User notes This dataset can be used to analyze, design, and optimize PSC, as it contains a considerable number of simulations (18.570) with their respective photovoltaic performance values, which is useful for studying the relationships between design parameters and electrical performance. Because of its composition and variety in the parameters that constitute the device, the dataset can be used to train different ML models using supervised methods such as random forest, gradient boosting, support vector machines (SVM), and deep neural networks to predict the performance metrics Voc, Jsc, FF, and PCE, or unsupervised techniques like clustering or dimensionality reduction (PCA, t-SNE) that allow discovering patterns or relationships between parameters. Additionally, multi-objective optimization techniques can be implemented, such as genetic algorithms, Bayesian methods, or particle swarm methods. On the other hand, as each input represents a set of specific parameters along with their performance results, researchers can identify optimal regions of the design space to maximize efficiency, minimize recombination losses, or reduce the use of high-cost materials. Thus, it can be useful in reverse design strategies to determine optimal configurations based on defined objectives. Additionally, it is important to mention that this database was generated for planar PSC with a single absorbing layer, which may represent limitations that should be considered when using it. Moreover, all the simulations were generated under constant and one-dimensional conditions, so three-dimensional effects, long-term degradation, or real environmental conditions (temperature, humidity, material degradation, etc) are not capture. Although a cross-validation was conducted with data reported in the literature, there may be discrepancies attributable to the lack of detail in the parameters reported by some authors, which prevents an exact replication of the experimental results. Finally, some parametric combinations were discarded due to numerical convergence failures, which may slightly bias the exploration of the design space. Author contributions Y.V.-G.: conceptualization, methodology, validation, investigation, data curation, and writing—original draft preparation, E.G.-V.: conceptualization, methodology, validation, formal analysis, investigation, data curation, and writing—original draft preparation, visualization, and supervision. A.S.-S.: conceptualization, formal analysis, investigation, data curation, and writing—review and editing, project administration, supervision, and funding acquisition. N.G.-C.: conceptualization, methodology, formal analysis, investigation, resources, writing—review and editing, supervision, project administration, and funding acquisition. All authors have read and agreed to the published version of the manuscript. Data availability Open Science Framework (OSF). Synthetic dataset to study the performance of perovskite solar cell simulations. DOI: 10.17605/OSF.IO/ZX4AJ . 71 This project contains the following underlying data: Synthetic dataset to study the performance of perovskite solar cell simulations.xlsx. All simulated device configurations and their corresponding photovoltaic performance metrics (Voc, Jsc, FF, PCE) were generated with SCAPS-1D under the conditions described in the methods. Data are available under the terms of the CC-By Attribution 4.0 International . 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Oishi AH, Anjum MT, Islam MM, et al. : Impact of absorber layer thickness on perovskite solar cell efficiency: A performance analysis. European Journal of Electrical Engineering and Computer Science. 2023; 7 : 48–51. Publisher Full Text Reference Source 69. Tress W: Metal halide perovskites as next-generation photovoltaic materials. Energy Environ. Sci. 2017; 10 : 951–969. 70. Snaith HJ: Present status and future prospects of perovskite photovoltaics. Nat. Mater. 2018; 17 : 372–376. PubMed Abstract | Publisher Full Text 71. Galvis YV, Valencia EG, Cardona NG, et al. : Synthetic dataset to study the performance of perovskite solar cell simulations.2025. Publisher Full Text Reference Source Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 22 Sep 2025 ADD YOUR COMMENT Comment Author details Author details 1 Departamento de Electrónica y Telecomunicaciones, Instituto Tecnológico Metropolitano, Medellín, Antioquia, 050034, Colombia 2 Escuela de Ingenierías Eléctrica, Electrónica y de Telecomunicaciones, Universidad Industrial de Santander, Bucaramanga, Santander, 680002, Colombia Yeraldin Velez-Galvis Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Visualization, Writing – Original Draft Preparation Esteban Gonzalez-Valencia Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing – Original Draft Preparation Alexander Sepulveda-Sepulveda Roles: Conceptualization, Formal Analysis, Funding Acquisition, Project Administration, Supervision, Validation, Writing – Review & Editing Nelson Gomez-Cardona Roles: Conceptualization, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Visualization, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This work was funded by MinCiencias-Colombia, with resources administered by ICETEX-Colombia (project code 2022-0724) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (2) version 2 Revised Published: 22 Oct 2025, 14:961 https://doi.org/10.12688/f1000research.168996.2 version 1 Published: 22 Sep 2025, 14:961 https://doi.org/10.12688/f1000research.168996.1 Copyright © 2025 Velez-Galvis Y et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Velez-Galvis Y, Gonzalez-Valencia E, Sepulveda-Sepulveda A and Gomez-Cardona N. Synthetic dataset to study the performance of perovskite solar cell simulations [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :961 ( https://doi.org/10.12688/f1000research.168996.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 22 Sep 2025 Views 0 Cite How to cite this report: Hussain E. Reviewer Report For: Synthetic dataset to study the performance of perovskite solar cell simulations [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :961 ( https://doi.org/10.5256/f1000research.186250.r417139 ) The direct URL for this report is: https://f1000research.com/articles/14-961/v1#referee-response-417139 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 30 Sep 2025 Ejaz Hussain , The Islamia University of Bahawalpur, Bahawalpur, Pakistan Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.186250.r417139 The manuscript presents a synthetic dataset of 18,570 simulated perovskite solar cells generated using SCAPS-1D. The dataset is highly valuable for the photovoltaic community, especially for training machine learning models and exploring optimization strategies. The study is well-structured, with ... Continue reading READ ALL The manuscript presents a synthetic dataset of 18,570 simulated perovskite solar cells generated using SCAPS-1D. The dataset is highly valuable for the photovoltaic community, especially for training machine learning models and exploring optimization strategies. The study is well-structured, with detailed methodology, validation, and dataset description. However, several aspects require major revisions before acceptance. The introduction is overloaded with citations and could be more concise. The methods lack sufficient justification for parameter ranges and convergence criteria. Figures could be more clearly labeled and discussed. Additionally, the limitations of using purely simulated data under idealized conditions should be emphasized. It should be considered for indexing after major revisions my comments are listed below: The introduction is informative but too lengthy consider condensing background citations and focusing on the novelty of work specifically. Clearly state the main research question or objective in the last paragraph of the introduction. Provide justification for selecting the specific absorber materials (MAPbI 3 , MASnI 3 , FAPbI 3 ). Clarify how parameter variation ranges in Table 1 were chosen (literature benchmarks or preliminary simulations?). In the validation section, include quantitative error analysis (e.g., RMSE, % deviation) between simulated and reported values. Some figures (e.g., distributions in Figure 2) need clearer labeling and more descriptive captions. Explicitly highlight the novelty of the dataset compared to existing open databases (e.g., NREL, FAIR databases). Discuss dataset limitations more critically (e.g., ignoring 3D effects, degradation, or experimental uncertainties). Consider providing examples of trained ML models as a demonstration of dataset utility. Revise for language consistency; some sentences are long and complex. Ensure uniform units in tables (μm, cm –3 , etc.) and check the formatting style. The methods section would benefit by providing a schematic workflow of dataset generation. Is the rationale for creating the dataset(s) clearly described? Partly Are the protocols appropriate and is the work technically sound? Yes Are sufficient details of methods and materials provided to allow replication by others? Partly Are the datasets clearly presented in a useable and accessible format? Yes Competing Interests: No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Hussain E. Reviewer Report For: Synthetic dataset to study the performance of perovskite solar cell simulations [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :961 ( https://doi.org/10.5256/f1000research.186250.r417139 ) The direct URL for this report is: https://f1000research.com/articles/14-961/v1#referee-response-417139 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 22 Oct 2025 Yeraldin Velez , Departamento de Electrónica y Telecomunicaciones, Instituto Tecnológico Metropolitano, Medellín, 050034, Colombia 22 Oct 2025 Author Response Dear, Dr. Ejaz Hussain, we greatly appreciate your comments that help us to improve the clarity and impact of our paper. On behalf of all the authors, I would like ... Continue reading Dear, Dr. Ejaz Hussain, we greatly appreciate your comments that help us to improve the clarity and impact of our paper. On behalf of all the authors, I would like to thank you for the time and effort you dedicated to reviewing our manuscript. We deeply appreciate your thoughtful comments, constructive suggestions, and valuable insights, which have been helpful to improve the clarity of our work. Your expertise in the field is highly respected, and your recommendations have not only helped us strengthen the scientific rigor of the manuscript but also guided us toward presenting our results in a way that increases the potential impact of the article within the research community. We are truly grateful for your contribution to enhance our study through your feedback. Comments: 1. The introduction is informative but too lengthy consider condensing background citations and focusing on the novelty of work specifically. Response: We make shorter descriptions of data that could be verified using the references in the second paragraph of the introduction. In the new version we have restructured the introduction to make it more compact. These modifications were made in paragraph 2 of the introduction, from line 9 onwards. 2. Clearly state the main research question or objective in the last paragraph of the introduction. Response: We focus on the contribution of this work and the objective of this research, where the modifications were made in the third paragraph starting at line 2. 3. Provide justification for selecting the specific absorber materials (MAPbI 3 , MASnI 3 , FAPbI 3 ). Response: Section 2, methods, paragraph 1, lines 7 to 10, explains that these materials were selected based on their extensive use by the scientific community and their high energy efficiency ratings. 4. Clarify how parameter variation ranges in Table 1 were chosen (literature benchmarks or preliminary simulations?). Response: We based our findings on the results reported in scientific literature for each specific layer. Section 2.3, Simulation data generation, paragraph 2, lines 13 to 15, states that all values were taken from research articles. 5. In the validation section, include quantitative error analysis (e.g., RMSE, % deviation) between simulated and reported values. Response: Section 2.4, paragraph 2, includes the RMSE and MAPE values for the values presented in Table 2 and provides a brief description of them, giving the reader an idea of why the values are presented. 6. Some figures (e.g., distributions in Figure 2) need clearer labeling and more descriptive captions. Response: In Figure 2, the caption was rewritten to be more descriptive, and the labels were adjusted for better visualization. 7. Explicitly highlight the novelty of the dataset compared to existing open databases (e.g., NREL, FAIR databases). Response: In section 3, paragraph 4, immediately after figure two, the importance of the generated database and its value in finding experimental databases in the literature but without considering their photoelectric parameters is explained. 8. Discuss dataset limitations more critically (e.g., ignoring 3D effects, degradation, or experimental uncertainties). Response: In section 3, paragraph 5, the limitations of the dataset are detailed, as it is based only on simulated data, without considering degradation due to environmental factors, losses due to surface defects, 3D analysis, or temperature changes, where its value is highlighted by allowing the interaction of cell performance to be studied when modifying each of the characteristics. 9. Consider providing examples of trained ML models as a demonstration of dataset utility. Response: We appreciate your comment about the need to study the dataset through a machine learning model. We are currently developing a methodology focused on predicting the performance of perovskite solar cells using different machine learning models, analyzing which technique would best suit the multi-output problem presented in this case. Due to the nature of the work, we decided to address it in detail in a separate, more extensive study. 10. Revise for language consistency; some sentences are long and complex. Response: We highly appreciate this comment and verified the structure of sentences to improve the coherence and understanding of our work. 11. Ensure uniform units in tables (μ m, cm –3 , etc.) and check the formatting style. Response: We have conducted a thorough review of the entire manuscript. Complex sentences have been simplified to improve readability, and the formatting of units in the text has been verified to ensure consistency throughout the document. 12. The methods section would benefit by providing a schematic workflow of dataset generation. Response: After the first paragraph of Section 2.3, Simulation Data Generation, we have included a new schematic workflow that describes, step by step, the process followed to generate the dataset. This improves the clarity and reproducibility of our procedure. We hope that these revisions satisfactorily address your comments. We thank you again for your guidance, which has been instrumental in strengthening our manuscript. With great respect and appreciation, Yeraldin Velez Galvis On behalf of all the authors Dear, Dr. Ejaz Hussain, we greatly appreciate your comments that help us to improve the clarity and impact of our paper. On behalf of all the authors, I would like to thank you for the time and effort you dedicated to reviewing our manuscript. We deeply appreciate your thoughtful comments, constructive suggestions, and valuable insights, which have been helpful to improve the clarity of our work. Your expertise in the field is highly respected, and your recommendations have not only helped us strengthen the scientific rigor of the manuscript but also guided us toward presenting our results in a way that increases the potential impact of the article within the research community. We are truly grateful for your contribution to enhance our study through your feedback. Comments: 1. The introduction is informative but too lengthy consider condensing background citations and focusing on the novelty of work specifically. Response: We make shorter descriptions of data that could be verified using the references in the second paragraph of the introduction. In the new version we have restructured the introduction to make it more compact. These modifications were made in paragraph 2 of the introduction, from line 9 onwards. 2. Clearly state the main research question or objective in the last paragraph of the introduction. Response: We focus on the contribution of this work and the objective of this research, where the modifications were made in the third paragraph starting at line 2. 3. Provide justification for selecting the specific absorber materials (MAPbI 3 , MASnI 3 , FAPbI 3 ). Response: Section 2, methods, paragraph 1, lines 7 to 10, explains that these materials were selected based on their extensive use by the scientific community and their high energy efficiency ratings. 4. Clarify how parameter variation ranges in Table 1 were chosen (literature benchmarks or preliminary simulations?). Response: We based our findings on the results reported in scientific literature for each specific layer. Section 2.3, Simulation data generation, paragraph 2, lines 13 to 15, states that all values were taken from research articles. 5. In the validation section, include quantitative error analysis (e.g., RMSE, % deviation) between simulated and reported values. Response: Section 2.4, paragraph 2, includes the RMSE and MAPE values for the values presented in Table 2 and provides a brief description of them, giving the reader an idea of why the values are presented. 6. Some figures (e.g., distributions in Figure 2) need clearer labeling and more descriptive captions. Response: In Figure 2, the caption was rewritten to be more descriptive, and the labels were adjusted for better visualization. 7. Explicitly highlight the novelty of the dataset compared to existing open databases (e.g., NREL, FAIR databases). Response: In section 3, paragraph 4, immediately after figure two, the importance of the generated database and its value in finding experimental databases in the literature but without considering their photoelectric parameters is explained. 8. Discuss dataset limitations more critically (e.g., ignoring 3D effects, degradation, or experimental uncertainties). Response: In section 3, paragraph 5, the limitations of the dataset are detailed, as it is based only on simulated data, without considering degradation due to environmental factors, losses due to surface defects, 3D analysis, or temperature changes, where its value is highlighted by allowing the interaction of cell performance to be studied when modifying each of the characteristics. 9. Consider providing examples of trained ML models as a demonstration of dataset utility. Response: We appreciate your comment about the need to study the dataset through a machine learning model. We are currently developing a methodology focused on predicting the performance of perovskite solar cells using different machine learning models, analyzing which technique would best suit the multi-output problem presented in this case. Due to the nature of the work, we decided to address it in detail in a separate, more extensive study. 10. Revise for language consistency; some sentences are long and complex. Response: We highly appreciate this comment and verified the structure of sentences to improve the coherence and understanding of our work. 11. Ensure uniform units in tables (μ m, cm –3 , etc.) and check the formatting style. Response: We have conducted a thorough review of the entire manuscript. Complex sentences have been simplified to improve readability, and the formatting of units in the text has been verified to ensure consistency throughout the document. 12. The methods section would benefit by providing a schematic workflow of dataset generation. Response: After the first paragraph of Section 2.3, Simulation Data Generation, we have included a new schematic workflow that describes, step by step, the process followed to generate the dataset. This improves the clarity and reproducibility of our procedure. We hope that these revisions satisfactorily address your comments. We thank you again for your guidance, which has been instrumental in strengthening our manuscript. With great respect and appreciation, Yeraldin Velez Galvis On behalf of all the authors Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 22 Oct 2025 Yeraldin Velez , Departamento de Electrónica y Telecomunicaciones, Instituto Tecnológico Metropolitano, Medellín, 050034, Colombia 22 Oct 2025 Author Response Dear, Dr. Ejaz Hussain, we greatly appreciate your comments that help us to improve the clarity and impact of our paper. On behalf of all the authors, I would like ... Continue reading Dear, Dr. Ejaz Hussain, we greatly appreciate your comments that help us to improve the clarity and impact of our paper. On behalf of all the authors, I would like to thank you for the time and effort you dedicated to reviewing our manuscript. We deeply appreciate your thoughtful comments, constructive suggestions, and valuable insights, which have been helpful to improve the clarity of our work. Your expertise in the field is highly respected, and your recommendations have not only helped us strengthen the scientific rigor of the manuscript but also guided us toward presenting our results in a way that increases the potential impact of the article within the research community. We are truly grateful for your contribution to enhance our study through your feedback. Comments: 1. The introduction is informative but too lengthy consider condensing background citations and focusing on the novelty of work specifically. Response: We make shorter descriptions of data that could be verified using the references in the second paragraph of the introduction. In the new version we have restructured the introduction to make it more compact. These modifications were made in paragraph 2 of the introduction, from line 9 onwards. 2. Clearly state the main research question or objective in the last paragraph of the introduction. Response: We focus on the contribution of this work and the objective of this research, where the modifications were made in the third paragraph starting at line 2. 3. Provide justification for selecting the specific absorber materials (MAPbI 3 , MASnI 3 , FAPbI 3 ). Response: Section 2, methods, paragraph 1, lines 7 to 10, explains that these materials were selected based on their extensive use by the scientific community and their high energy efficiency ratings. 4. Clarify how parameter variation ranges in Table 1 were chosen (literature benchmarks or preliminary simulations?). Response: We based our findings on the results reported in scientific literature for each specific layer. Section 2.3, Simulation data generation, paragraph 2, lines 13 to 15, states that all values were taken from research articles. 5. In the validation section, include quantitative error analysis (e.g., RMSE, % deviation) between simulated and reported values. Response: Section 2.4, paragraph 2, includes the RMSE and MAPE values for the values presented in Table 2 and provides a brief description of them, giving the reader an idea of why the values are presented. 6. Some figures (e.g., distributions in Figure 2) need clearer labeling and more descriptive captions. Response: In Figure 2, the caption was rewritten to be more descriptive, and the labels were adjusted for better visualization. 7. Explicitly highlight the novelty of the dataset compared to existing open databases (e.g., NREL, FAIR databases). Response: In section 3, paragraph 4, immediately after figure two, the importance of the generated database and its value in finding experimental databases in the literature but without considering their photoelectric parameters is explained. 8. Discuss dataset limitations more critically (e.g., ignoring 3D effects, degradation, or experimental uncertainties). Response: In section 3, paragraph 5, the limitations of the dataset are detailed, as it is based only on simulated data, without considering degradation due to environmental factors, losses due to surface defects, 3D analysis, or temperature changes, where its value is highlighted by allowing the interaction of cell performance to be studied when modifying each of the characteristics. 9. Consider providing examples of trained ML models as a demonstration of dataset utility. Response: We appreciate your comment about the need to study the dataset through a machine learning model. We are currently developing a methodology focused on predicting the performance of perovskite solar cells using different machine learning models, analyzing which technique would best suit the multi-output problem presented in this case. Due to the nature of the work, we decided to address it in detail in a separate, more extensive study. 10. Revise for language consistency; some sentences are long and complex. Response: We highly appreciate this comment and verified the structure of sentences to improve the coherence and understanding of our work. 11. Ensure uniform units in tables (μ m, cm –3 , etc.) and check the formatting style. Response: We have conducted a thorough review of the entire manuscript. Complex sentences have been simplified to improve readability, and the formatting of units in the text has been verified to ensure consistency throughout the document. 12. The methods section would benefit by providing a schematic workflow of dataset generation. Response: After the first paragraph of Section 2.3, Simulation Data Generation, we have included a new schematic workflow that describes, step by step, the process followed to generate the dataset. This improves the clarity and reproducibility of our procedure. We hope that these revisions satisfactorily address your comments. We thank you again for your guidance, which has been instrumental in strengthening our manuscript. With great respect and appreciation, Yeraldin Velez Galvis On behalf of all the authors Dear, Dr. Ejaz Hussain, we greatly appreciate your comments that help us to improve the clarity and impact of our paper. On behalf of all the authors, I would like to thank you for the time and effort you dedicated to reviewing our manuscript. We deeply appreciate your thoughtful comments, constructive suggestions, and valuable insights, which have been helpful to improve the clarity of our work. Your expertise in the field is highly respected, and your recommendations have not only helped us strengthen the scientific rigor of the manuscript but also guided us toward presenting our results in a way that increases the potential impact of the article within the research community. We are truly grateful for your contribution to enhance our study through your feedback. Comments: 1. The introduction is informative but too lengthy consider condensing background citations and focusing on the novelty of work specifically. Response: We make shorter descriptions of data that could be verified using the references in the second paragraph of the introduction. In the new version we have restructured the introduction to make it more compact. These modifications were made in paragraph 2 of the introduction, from line 9 onwards. 2. Clearly state the main research question or objective in the last paragraph of the introduction. Response: We focus on the contribution of this work and the objective of this research, where the modifications were made in the third paragraph starting at line 2. 3. Provide justification for selecting the specific absorber materials (MAPbI 3 , MASnI 3 , FAPbI 3 ). Response: Section 2, methods, paragraph 1, lines 7 to 10, explains that these materials were selected based on their extensive use by the scientific community and their high energy efficiency ratings. 4. Clarify how parameter variation ranges in Table 1 were chosen (literature benchmarks or preliminary simulations?). Response: We based our findings on the results reported in scientific literature for each specific layer. Section 2.3, Simulation data generation, paragraph 2, lines 13 to 15, states that all values were taken from research articles. 5. In the validation section, include quantitative error analysis (e.g., RMSE, % deviation) between simulated and reported values. Response: Section 2.4, paragraph 2, includes the RMSE and MAPE values for the values presented in Table 2 and provides a brief description of them, giving the reader an idea of why the values are presented. 6. Some figures (e.g., distributions in Figure 2) need clearer labeling and more descriptive captions. Response: In Figure 2, the caption was rewritten to be more descriptive, and the labels were adjusted for better visualization. 7. Explicitly highlight the novelty of the dataset compared to existing open databases (e.g., NREL, FAIR databases). Response: In section 3, paragraph 4, immediately after figure two, the importance of the generated database and its value in finding experimental databases in the literature but without considering their photoelectric parameters is explained. 8. Discuss dataset limitations more critically (e.g., ignoring 3D effects, degradation, or experimental uncertainties). Response: In section 3, paragraph 5, the limitations of the dataset are detailed, as it is based only on simulated data, without considering degradation due to environmental factors, losses due to surface defects, 3D analysis, or temperature changes, where its value is highlighted by allowing the interaction of cell performance to be studied when modifying each of the characteristics. 9. Consider providing examples of trained ML models as a demonstration of dataset utility. Response: We appreciate your comment about the need to study the dataset through a machine learning model. We are currently developing a methodology focused on predicting the performance of perovskite solar cells using different machine learning models, analyzing which technique would best suit the multi-output problem presented in this case. Due to the nature of the work, we decided to address it in detail in a separate, more extensive study. 10. Revise for language consistency; some sentences are long and complex. Response: We highly appreciate this comment and verified the structure of sentences to improve the coherence and understanding of our work. 11. Ensure uniform units in tables (μ m, cm –3 , etc.) and check the formatting style. Response: We have conducted a thorough review of the entire manuscript. Complex sentences have been simplified to improve readability, and the formatting of units in the text has been verified to ensure consistency throughout the document. 12. The methods section would benefit by providing a schematic workflow of dataset generation. Response: After the first paragraph of Section 2.3, Simulation Data Generation, we have included a new schematic workflow that describes, step by step, the process followed to generate the dataset. This improves the clarity and reproducibility of our procedure. We hope that these revisions satisfactorily address your comments. We thank you again for your guidance, which has been instrumental in strengthening our manuscript. With great respect and appreciation, Yeraldin Velez Galvis On behalf of all the authors Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 22 Sep 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 2 (revision) 22 Oct 25 read read Version 1 22 Sep 25 read Ejaz Hussain , The Islamia University of Bahawalpur, Bahawalpur, Pakistan Tarekuzzaman Md , International Islamic University Chittagong, Chittagong, Bangladesh Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Md T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 20 Jan 2026 | for Version 2 Tarekuzzaman Md , International Islamic University Chittagong, Chittagong, Chittagong Division, Bangladesh 0 Views copyright © 2026 Md T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript entitled “Synthetic dataset to study the performance of perovskite solar cell simulations”. The manuscript is generally well presented; however, several important issues still need to be addressed before it can be considered for indexing. What is the key novelty of this dataset compared to existing open databases such as NREL or FAIR perovskite repositories? How does the inclusion of device-level physical parameters (e.g., defect density, dielectric permittivity) advance PSC research beyond performance-only datasets? How does the dataset address the trade-off between physical realism and computational scalability? What criteria were used to decide which parameter combinations led to “non-physical” or non-convergent simulations? Were interface defect states or recombination velocities considered, and if not, how might this omission affect FF predictions? How were the parameter ranges in Table 1 validated against experimental feasibility? Were correlations between parameters (e.g., thickness and defect density) considered or treated as independent? Please include the SQ limit curve and discuss PV metrics accordingly. Do the chosen ranges adequately capture high-efficiency device regimes, or are they biased toward mid-performance devices? How might ignoring degradation, humidity, temperature variation, and 3D effects impact predictive outcomes? The English is good but can be polished further by reading through the manuscript carefully, ensuring proper spacing after full stops and spelling errors. Is the rationale for creating the dataset(s) clearly described? Partly Are the protocols appropriate and is the work technically sound? Yes Are sufficient details of methods and materials provided to allow replication by others? Partly Are the datasets clearly presented in a useable and accessible format? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Density Functional theory, Solar cell, machine learning I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Md T. Peer Review Report For: Synthetic dataset to study the performance of perovskite solar cell simulations [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :961 ( https://doi.org/10.5256/f1000research.189916.r449762) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-961/v2#referee-response-449762 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Hussain E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 06 Nov 2025 | for Version 2 Ejaz Hussain , The Islamia University of Bahawalpur, Bahawalpur, Pakistan 0 Views copyright © 2025 Hussain E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Authors have resolved all issues in the manuscript. It can now be indexed for publication. Competing Interests No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Hussain E. Peer Review Report For: Synthetic dataset to study the performance of perovskite solar cell simulations [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :961 ( https://doi.org/10.5256/f1000research.189916.r426082) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-961/v2#referee-response-426082 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Hussain E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 30 Sep 2025 | for Version 1 Ejaz Hussain , The Islamia University of Bahawalpur, Bahawalpur, Pakistan 0 Views copyright © 2025 Hussain E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript presents a synthetic dataset of 18,570 simulated perovskite solar cells generated using SCAPS-1D. The dataset is highly valuable for the photovoltaic community, especially for training machine learning models and exploring optimization strategies. The study is well-structured, with detailed methodology, validation, and dataset description. However, several aspects require major revisions before acceptance. The introduction is overloaded with citations and could be more concise. The methods lack sufficient justification for parameter ranges and convergence criteria. Figures could be more clearly labeled and discussed. Additionally, the limitations of using purely simulated data under idealized conditions should be emphasized. It should be considered for indexing after major revisions my comments are listed below: The introduction is informative but too lengthy consider condensing background citations and focusing on the novelty of work specifically. Clearly state the main research question or objective in the last paragraph of the introduction. Provide justification for selecting the specific absorber materials (MAPbI 3 , MASnI 3 , FAPbI 3 ). Clarify how parameter variation ranges in Table 1 were chosen (literature benchmarks or preliminary simulations?). In the validation section, include quantitative error analysis (e.g., RMSE, % deviation) between simulated and reported values. Some figures (e.g., distributions in Figure 2) need clearer labeling and more descriptive captions. Explicitly highlight the novelty of the dataset compared to existing open databases (e.g., NREL, FAIR databases). Discuss dataset limitations more critically (e.g., ignoring 3D effects, degradation, or experimental uncertainties). Consider providing examples of trained ML models as a demonstration of dataset utility. Revise for language consistency; some sentences are long and complex. Ensure uniform units in tables (μm, cm –3 , etc.) and check the formatting style. The methods section would benefit by providing a schematic workflow of dataset generation. Is the rationale for creating the dataset(s) clearly described? Partly Are the protocols appropriate and is the work technically sound? Yes Are sufficient details of methods and materials provided to allow replication by others? Partly Are the datasets clearly presented in a useable and accessible format? Yes Competing Interests No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 22 Oct 2025 Yeraldin Velez, Departamento de Electrónica y Telecomunicaciones, Instituto Tecnológico Metropolitano, Medellín, 050034, Colombia Dear, Dr. Ejaz Hussain, we greatly appreciate your comments that help us to improve the clarity and impact of our paper. On behalf of all the authors, I would like to thank you for the time and effort you dedicated to reviewing our manuscript. We deeply appreciate your thoughtful comments, constructive suggestions, and valuable insights, which have been helpful to improve the clarity of our work. Your expertise in the field is highly respected, and your recommendations have not only helped us strengthen the scientific rigor of the manuscript but also guided us toward presenting our results in a way that increases the potential impact of the article within the research community. We are truly grateful for your contribution to enhance our study through your feedback. Comments: 1. The introduction is informative but too lengthy consider condensing background citations and focusing on the novelty of work specifically. Response: We make shorter descriptions of data that could be verified using the references in the second paragraph of the introduction. In the new version we have restructured the introduction to make it more compact. These modifications were made in paragraph 2 of the introduction, from line 9 onwards. 2. Clearly state the main research question or objective in the last paragraph of the introduction. Response: We focus on the contribution of this work and the objective of this research, where the modifications were made in the third paragraph starting at line 2. 3. Provide justification for selecting the specific absorber materials (MAPbI 3 , MASnI 3 , FAPbI 3 ). Response: Section 2, methods, paragraph 1, lines 7 to 10, explains that these materials were selected based on their extensive use by the scientific community and their high energy efficiency ratings. 4. Clarify how parameter variation ranges in Table 1 were chosen (literature benchmarks or preliminary simulations?). Response: We based our findings on the results reported in scientific literature for each specific layer. Section 2.3, Simulation data generation, paragraph 2, lines 13 to 15, states that all values were taken from research articles. 5. In the validation section, include quantitative error analysis (e.g., RMSE, % deviation) between simulated and reported values. Response: Section 2.4, paragraph 2, includes the RMSE and MAPE values for the values presented in Table 2 and provides a brief description of them, giving the reader an idea of why the values are presented. 6. Some figures (e.g., distributions in Figure 2) need clearer labeling and more descriptive captions. Response: In Figure 2, the caption was rewritten to be more descriptive, and the labels were adjusted for better visualization. 7. Explicitly highlight the novelty of the dataset compared to existing open databases (e.g., NREL, FAIR databases). Response: In section 3, paragraph 4, immediately after figure two, the importance of the generated database and its value in finding experimental databases in the literature but without considering their photoelectric parameters is explained. 8. Discuss dataset limitations more critically (e.g., ignoring 3D effects, degradation, or experimental uncertainties). Response: In section 3, paragraph 5, the limitations of the dataset are detailed, as it is based only on simulated data, without considering degradation due to environmental factors, losses due to surface defects, 3D analysis, or temperature changes, where its value is highlighted by allowing the interaction of cell performance to be studied when modifying each of the characteristics. 9. Consider providing examples of trained ML models as a demonstration of dataset utility. Response: We appreciate your comment about the need to study the dataset through a machine learning model. We are currently developing a methodology focused on predicting the performance of perovskite solar cells using different machine learning models, analyzing which technique would best suit the multi-output problem presented in this case. Due to the nature of the work, we decided to address it in detail in a separate, more extensive study. 10. Revise for language consistency; some sentences are long and complex. Response: We highly appreciate this comment and verified the structure of sentences to improve the coherence and understanding of our work. 11. Ensure uniform units in tables (μ m, cm –3 , etc.) and check the formatting style. Response: We have conducted a thorough review of the entire manuscript. Complex sentences have been simplified to improve readability, and the formatting of units in the text has been verified to ensure consistency throughout the document. 12. The methods section would benefit by providing a schematic workflow of dataset generation. Response: After the first paragraph of Section 2.3, Simulation Data Generation, we have included a new schematic workflow that describes, step by step, the process followed to generate the dataset. This improves the clarity and reproducibility of our procedure. We hope that these revisions satisfactorily address your comments. We thank you again for your guidance, which has been instrumental in strengthening our manuscript. With great respect and appreciation, Yeraldin Velez Galvis On behalf of all the authors View more View less Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. reply Respond Report a concern Hussain E. Peer Review Report For: Synthetic dataset to study the performance of perovskite solar cell simulations [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :961 ( https://doi.org/10.5256/f1000research.186250.r417139) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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