Synthetic dataset to study the performance of perovskite solar cell simulations

Yeraldin Velez-Galvis; Esteban Gonzalez-Valencia; Alexander Sepulveda-Sepulveda; Nelson Gomez-Cardona

doi:10.12688/f1000research.168996.1

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Synthetic dataset to study the performance of perovskite solar cell simulations

[version 1; peer review: awaiting peer review]

Yeraldin Velez-Galvis ¹, Esteban Gonzalez-Valencia¹, Alexander Sepulveda-Sepulveda², Nelson Gomez-Cardona¹

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

REVIEWER STATUS AWAITING PEER REVIEW

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.

Keywords

Pervoskite solar cell, SCAPS-1D, dataset, photovoltaic technology, numerical simulation, machine learning.

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: awaiting peer review]. 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 Sep 2025, 14:961 (https://doi.org/10.12688/f1000research.168996.1)

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 PSC³³ 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)

\frac{\partial}{\partial x} (ε_{0} ε_{r} \frac{\partial ψ}{\partial x}) = - q (p - n + N_{D}^{+} - N_{A}^{-} + \frac{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)

- \frac{\partial J_{n}}{\partial x} - U_{n} + G = \frac{\partial n}{\partial t},

(3)

- \frac{\partial J_{p}}{\partial x} - U_{p} + G = \frac{\partial p}{\partial 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} = \int_{0}^{λ_{\max}} q ϕ (λ) EQE (λ) dλ

(5)

V_{OC} = \frac{kT}{q} ln (\frac{J_{SC}}{J_{0}} + 1),

(6)

FF = \frac{V_{mp} \cdot J_{mp}}{V_{OC} \cdot J_{SC}},

(7)

PCE = \frac{V_{OC} \cdot J_{SC} \cdot 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 limit⁵²; 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 Voc⁵⁵; 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	μm
HTL	T_HTL	0.1 – 0.7	μm
Perovskite	T_PVK	0.1 – 1.7	μm
	E_g	1.1 – 1.9	eV
	$ε_{r}$	8 – 20	–
	N_A	1 × 10¹³ – 1 × 10¹⁷	cm⁻³
	N_t	4 × 10¹³ – 4 × 10¹⁵	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 limit⁵⁷ 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
Reference	V_OC (V)	J_SC (mA/cm²)	FF (%)	PCE (%)	V_OC (V)	J_SC (mA/cm²)	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.

Acknowledgements

The authors are grateful to Marc Burgelman and his colleagues at the University of Gent, Belgium, for providing the SCAPS-1D simulator and gratefully acknowledge Prof. Monica Botero Londoño (School of Electrical, Electronic and Telecommunications Engineering, Universidad Industrial de Santander) for her valuable guidance in defining the set of parameters explored in this work.

References

1. Galagan Y: Perovskite solar cells from lab to fab: the main challenges to access the market. Oxford Open Materials Science. 2020; 1. 2633-6979. Publisher Full Text
2. Aydin E, Ugur E, Yildirim BK, et al.: Enhanced optoelectronic coupling for perovskite/silicon tandem solar cells. Nature. 2023; 623: 732–738. 0028-0836. PubMed Abstract | Publisher Full Text Reference Source
3. Khalid Hossain M, Samajdar DP, Das RC, et al.: Design and simulation of cs2biagi6 double perovskite solar cells with different electron transport layers for efficiency enhancement. Energy Fuel. 2023; 37: 3957–3979. 0887-0624. Publisher Full Text
4. Saxena P, Gorji NE: Comsol simulation of heat distribution in perovskite solar cells: Coupled optical–electrical–thermal 3-d analysis. IEEE Journal of Photovoltaics. 2019; 9: 1693–1698. 2156-3381. Publisher Full Text Reference Source
5. Karimi E, Ghorashi SMB, Hashemi M: International Journal of Optics and Photonics. 14: 57–66. 1735-8590. Publisher Full Text
6. Khalid Hossain M, Arnab AA, Das RC, et al.: Combined dft, scaps-1d, and wxamps frameworks for design optimization of efficient cs2biagi6-based perovskite solar cells with different charge transport layers. RSC Adv. 2022; 12: 34850–34873. 2046-2069. PubMed Abstract | Publisher Full Text | Free Full Text Reference Source
7. Shrivastav N, Madan J, Pandey R: Predicting photovoltaic efficiency in cs-based perovskite solar cells: A comprehensive study integrating scaps simulation and machine learning models. Solid State Commun. 2024; 380: 115437. 00381098. Publisher Full Text
8. Yang Z, Ye Q, Chu Z, et al.: Recent progress in high-efficiency planar-structure perovskite solar cells. Energy Environ. Mater. 2019; 2: 93–106. 2575-0356. Publisher Full Text
9. Elangovan NK, Kannadasan R, Beenarani BB, et al.: Recent developments in perovskite materials, fabrication techniques, band gap engineering, and the stability of perovskite solar cells. Energy Rep. 2024; 11: 1171–1190. 23524847. Publisher Full Text
10. Velez-Galvis Y, Sepulveda A, Echeverri-Perez J, et al.: Simulation of lead-free perovskite solar cell based on masni3 compared with fapbi3. Optica Latin America Optics and Photonics Conference (LAOP) 2024 (2024), paper Tu4A.16. 2024; page Tu4A.16. Publisher Full Text Reference Source
11. Stanić D, Kojić V, Bohač M, et al.: Simulation and optimization of fapbi3 perovskite solar cells with a batio3 layer for efficiency enhancement. Materials. 2022; 15. 19961944. PubMed Abstract | Publisher Full Text | Free Full Text
12. Assal S, Yadir S, Amiry H, et al.: Analysis of technological factors’s influence on solar cell’s performance by pc1d simulation. 2014 International Renewable and Sustainable Energy Conference (IRSEC). IEEE; 2014; pages 1–5. 978-1-4799-7336-1. Publisher Full Text Reference Source
13. Liu F, Zhu J, Wei J, et al.: Numerical simulation: Toward the design of high-efficiency planar perovskite solar cells. Appl. Phys. Lett. 2014; 104. 0003-6951. Publisher Full Text Reference Source
14. Alanazi TI, Eid OI, Okil M: Numerical study of flexible perovskite/si tandem solar cell using tcad simulation. Opt. Quant. Electron. 2023; 55: 1152. 0306-8919. Publisher Full Text
15. Deepthi Jayan K: Complete modelling and simulation of all perovskite tandem solar cells. Mater. Sci. Eng. B. 2023; 294: 116506. 09215107. Publisher Full Text Reference Source
16. Gong J: Simulation of steady-state characteristics of heterojunction perovskite solar cells in wxamps. Optik. 2021; 232: 166382. 00304026. Publisher Full Text Reference Source
17. Dadashbeik M, Fathi D, Eskandari M: Design and simulation of perovskite solar cells based on graphene and tio2/graphene nanocomposite as electron transport layer. Sol. Energy. 2020; 207: 917–924. 0038092X. Publisher Full Text Reference Source
18. Chabane H, Dehimi L, Bencherif H, et al.: Optimized al0.25ga0.75as solar cell performance using a new approach based on hybridizing silvaco tcad simulator with real coded genetic algorithm. J. Opt. 2024; pages 1–16. 0972-8821. Publisher Full Text
19. Jimoh OM, Ikechiamaka FN, Akinbolati A, et al.: Investigating the performance of perovskite solar cells using nickel oxide and copper iodide as ptype inorganic layers by scaps-1d simulation. Physics Access. 2022; 02: 37–50. 2756-3898. Publisher Full Text Reference Source
20. Abdelaziz S, Zekry A, Shaker A, et al.: Investigating the performance of formamidinium tin-based perovskite solar cell by scaps device simulation. Opt. Mater. 2020; 101: 109738. 09253467. Publisher Full Text Reference Source
21. Chabri I, Benhouria Y, Oubelkacem A, et al.: Scaps device simulation study of formamidinium tin-based perovskite solar cells: Investigating the influence of absorber parameters and transport layers on device performance. Sol. Energy. 2023; 262: 111846. 0038092X. Publisher Full Text Reference Source
22. Sanchez JEV, Londoño MAB, Sepulveda FAS, et al.: Absorber layer thickness as a new feature in statistical learning tools of perovskite solar cells. J. Appl. Res. Technol. 2023; 21: 858–865. 2448-6736. Publisher Full Text Reference Source
23. Zhan L, Ye D, Qiu X, et al.: Discovery of stable hybrid organic-inorganic double perovskites for high-performance solar cells via machine-learning algorithms and crystal graph convolution neural network method. arXiv. 2023. PubMed Abstract Reference Source
24. Azar MH, Aynehband S, Abdollahi H, et al.: Scaps empowered machine learning modelling of perovskite solar cells: Predictive design of active layer and hole transport materials. Photonics. 2023; 10: 271. 2304-6732. Publisher Full Text Reference Source
25. Rojas-Rincón C, Vélez-Galvis Y, Botero-Londoño M, et al.: Machine learning for the prediction of perovskite solar cell performance: A brief review. Congress on Research, Development and Innovation in Renewable Energies. Cham: Springer; 2025; pages 15–24. 978-3-031-88995-0. Publisher Full Text
26. Odabaşı Ç, Yıldırım R: Machine learning analysis on stability of perovskite solar cells. Sol. Energy Mater. Sol. Cells. 2020; 205: 110284. 09270248. Publisher Full Text Reference Source
27. Yan W, Liu Y, Zang Y, et al.: Machine learning enabled development of unexplored perovskite solar cells with high efficiency. Nano Energy. 2022; 99: 107394. 22112855. Publisher Full Text Reference Source
28. Yao L, Dong Wei W, Liu JM, et al.: Predicting the device performance of the perovskite solar cells from the experimental parameters through machine learning of existing experimental results. J. Energy Chem. 2023; 77: 200–208. 20954956. Publisher Full Text Reference Source
29. Mahmood A, Wang J-L: Machine learning for high performance organic solar cells: current scenario and future prospects. Energy Environ. Sci. 2021; 14: 90–105. 1754-5692. Publisher Full Text Reference Source
30. David TW, Anizelli H, Jacobsson TJ, et al.: Enhancing the stability of organic photovoltaics through machine learning. Nano Energy. 2020; 78: 105342. 22112855. Publisher Full Text Reference Source
31. Shimul AI, Khan M, Rayhan A, et al.: Machine learning-based optimization and performance enhancement of ch3nh3snbr3 perovskite solar cells with different charge transport materials using scaps-1d and wxamps. Advanced Theory and Simulations. 2025; 8. Publisher Full Text
32. Burgelman M: Scaps-1d: Solar cell capacitance simulator, 2005. Software for numerical simulation of photovoltaic devices.Reference Source
33. Ahmad S, Kazim S, Grätzel M: Perovskite Solar Cells. Wiley; 2021. 9783527347155. Publisher Full Text
34. Jacobsson TJ, Hultqvist A, García-Fernández A, et al.: An open-access database and analysis tool for perovskite solar cells based on the fair data principles. Nat. Energy. 2022; 7: 107–115. 20587546. Publisher Full Text
35. Benami A, Ouslimane T, Et-taya L, et al.: Comparison of the effects of zno and tio2 on the performance of perovskite solar cells via scaps-1d software package. J. Nano-Electron. Phys. 2022; 14: 01033-1–01033-5. 20776772. Publisher Full Text Reference Source
36. Homola T, Pospisil J, Shekargoftar M, et al.: Perovskite solar cells with low-cost tio2 mesoporous photoanodes prepared by rapid low-temperature (70 °c) plasma processing. ACS Appl Energy Mater. 2020; 3: 12009–12018. 2574-0962. Publisher Full Text
37. Afre RA, Pugliese D: Perovskite solar cells: A review of the latest advances in materials, fabrication techniques, and stability enhancement strategies.2024. 2072666X.
38. Zhang J, Zhang W, Cheng H-M, et al.: Critical review of recent progress of flexible perovskite solar cells. Mater. Today. 2020; 39: 66–88. 13697021. Publisher Full Text Reference Source
39. Devi C, Mehra R: Device simulation of lead-free masni3 solar cell with cusbs2 (copper antimony sulfide). J. Mater. Sci. 2020; 54: 5615–5624. 0022-2461. Publisher Full Text
40. Thakur D, Chiang S-E, Yang M-H, et al.: Self-stability of un-encapsulated polycrystalline mapbi3 solar cells via the formation of chemical bonds between c60 molecules and ma cations. Sol. Energy Mater. Sol. Cells. 2022; 235: 111454. 09270248. Publisher Full Text Reference Source
41. Shoab M, Aslam Z, Zulfequar M, et al.: Numerical interface optimization of lead-free perovskite solar cells (ch3nh3sni3) for 30 photo-conversion efficiency using scaps-1d. Next Materials. 2024; 4: 100200. 29498228. Publisher Full Text
42. Luo W, He J, Yu S, et al.: Optimizing the performance of tin-based perovskite solar cells employing 2d tungsten disulfide as an htl by numerical simulation. Phys. Status Solidi A. 2023; 220. 1862-6300. Publisher Full Text
43. Piñón AC, Reyes RC, Lázaro A, et al.: Study of a lead-free perovskite solar cell using czts as htl to achieve a 20. Micromachines. 2021; 12. 2072666X. PubMed Abstract | Publisher Full Text | Free Full Text
44. Banik S, Das A, Das BK, et al.: Numerical simulation and performance optimization of a lead-free inorganic perovskite solar cell using scaps-1d. Heliyon. 2024; 10: e23985. 24058440. PubMed Abstract | Publisher Full Text | Free Full Text Reference Source
45. COMSOL: Comsol multiphysics®.Reference Source
46. Kaur J, Sharma AK, Basu R, et al.: Simulation of planar heterojunction ch3nh3pbi3 solar cell employing sigesn alloy as a backplane. SILICON. 2024; 16: 1453–1466. 18769918. Publisher Full Text .
47. Izadi F, Ghobadi A, Gharaati A, et al.: Effect of interface defects on high efficient perovskite solar cells. Optik. 2021; 227: 166061. 00304026. Publisher Full Text
48. Ompong D, Clements M: Optimization of formamidinium-based perovskite solar cell using scaps-1d. Results in Optics. 2024; 14: 100611. 26669501. Publisher Full Text Reference Source
49. Bag A, Radhakrishnan R, Nekovei R, et al.: Effect of absorber layer, hole transport layer thicknesses, and its doping density on the performance of perovskite solar cells by device simulation. Sol. Energy. 2020; 196: 177–182. 0038092X. Publisher Full Text
50. Lee D, Kim K, Kim H-D: Thickness optimization of charge transport layers on perovskite solar cells for aerospace applications. Nanomaterials. 2023; 13: 1848. PubMed Abstract | Publisher Full Text | Free Full Text
51. Aseena S, Nelsa A, Suresh BV: Optimization of layer thickness of zno based perovskite solar cells using scaps 1d. Mater Today Proc. 2020; 43: 3432–3437. Publisher Full Text
52. Miah M, Mayeen Khandaker M, Rahman MA, et al.: Band gap tuning of perovskite solar cells for enhancing the efficiency and stability: issues and prospects. RSC Adv. 2024; 14: 15876–15906. PubMed Abstract | Publisher Full Text | Free Full Text
53. Yang Z, Surrente A, Gałkowski K, et al.: Unraveling the exciton binding energy and the dielectric constant in single crystal methylammonium lead triiodide perovskite. J. Phys. Chem. Lett. 2017; 8: 1851–1855. PubMed Abstract | Publisher Full Text
54. Rui S, Zhaojian X, Jiang W, et al.: Dielectric screening in perovskite photovoltaics. Nat. Commun. 2021; 12: 2479. PubMed Abstract | Publisher Full Text | Free Full Text
55. Sahoo A, Mohanty I, Mangal S: Effect of acceptor density, thickness and temperature on device performance for tin-based perovskite solar cell. Mater Today Proc. 2022; 62: 6210–6215. Publisher Full Text
56. Vaish S, Dixit SK: Study the effect of total defect density variation in absorbing layer on the power conversion efficiency of lead halide perovskite solar cell using scaps-1d simulation tool. Mater Today Proc. 2023; 91: 17–20. International Conference on Futuristic Materials. 2214-7853. Publisher Full Text Reference Source
57. Shockley W, Queisser HJ: Detailed balance limit of efficiency of p-n junction solar cells. J. Appl. Phys. 1961; 32(3): 510–519. 0021-8979. Publisher Full Text
58. National Renewable Energy Laboratory (NREL): Best research-cell efficiency chart.2025. Accessed 2025-08-01. Reference Source
59. Rahman MS, Ahmed N, Paul T, et al.: Performance evaluation of lead free ch3nh3sni3 perovskite solar cell: A simulation approach by scaps-1d. 2024 Third International Conference on Power, Control and Computing Technologies (ICPC2T). 2024; pages 392–397. Publisher Full Text
60. Mohammadi MH, Eskandari M, Fathi D: Design of optimized photonic-structure and analysis of adding a sio2 layer on the parallel ch3nh3pbi3/ch3nh3sni3 perovskite solar cells. Sci. Rep. 2023; 13: 15905. 20452322. PubMed Abstract | Publisher Full Text | Free Full Text
61. Ouslimane T, Et-taya L, Elmaimouni L, et al.: Impact of absorber layer thickness, defect density, and operating temperature on the performance of mapbi3 solar cells based on zno electron transporting material. Heliyon. 2021; 7: e06379. 24058440. PubMed Abstract | Publisher Full Text | Free Full Text Reference Source
62. Sharma AK, Kaushik DK: Numerical simulation of masni 3/cui heterojunction based perovskite solar cell. J. Phys. Conf. Ser. 2022; 2267: 012148. 1742-6588. Publisher Full Text .
63. Rana AD, Pharne ID, Bhargava K: Numerical simulation of highly efficient double perovskite solar cell using scaps-1d. Mater Today Proc. 2023; 73: 584–589. Nanomaterials for Energy Conversion and Storage Application-2022 (NECSA 2022). 2214-7853. Publisher Full Text Reference Source
64. Karthick S, Velumani S, Bouclé J: Experimental and scaps simulated formamidinium perovskite solar cells: A comparison of device performance. Sol. Energy. 2020; 205: 349–357. 0038092X. Publisher Full Text Reference Source
65. Minbashi M, Yazdani E: Comprehensive study of anomalous hysteresis behavior in perovskite-based solar cells. Sci. Rep. 2022; 12: 14916. 2045-2322. PubMed Abstract | Publisher Full Text | Free Full Text Reference Source
66. Stranks SD, Snaith HJ: Metal-halide perovskites for photovoltaic and light-emitting devices. Nat. Nanotechnol. 2015; 10: 391–402. Publisher Full Text
67. Jena AK, Kulkarni A, Miyasaka T: Halide perovskite photovoltaics: Background, status, and future prospects. Chem. Rev. 2019; 119: 3036–3103. PubMed Abstract | Publisher Full Text
68. 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

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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.

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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: awaiting peer review]. F1000Research 2025, 14:961 (https://doi.org/10.12688/f1000research.168996.1)

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[2] 2. Aydin E, Ugur E, Yildirim BK, et al.: Enhanced optoelectronic coupling for perovskite/silicon tandem solar cells. Nature. 2023; 623: 732–738. 0028-0836. PubMed Abstract | Publisher Full Text Reference Source

[3] 3. Khalid Hossain M, Samajdar DP, Das RC, et al.: Design and simulation of cs2biagi6 double perovskite solar cells with different electron transport layers for efficiency enhancement. Energy Fuel. 2023; 37: 3957–3979. 0887-0624. Publisher Full Text

[4] 4. Saxena P, Gorji NE: Comsol simulation of heat distribution in perovskite solar cells: Coupled optical–electrical–thermal 3-d analysis. IEEE Journal of Photovoltaics. 2019; 9: 1693–1698. 2156-3381. Publisher Full Text Reference Source

[5] 5. Karimi E, Ghorashi SMB, Hashemi M: International Journal of Optics and Photonics. 14: 57–66. 1735-8590. Publisher Full Text

[6] 6. Khalid Hossain M, Arnab AA, Das RC, et al.: Combined dft, scaps-1d, and wxamps frameworks for design optimization of efficient cs2biagi6-based perovskite solar cells with different charge transport layers. RSC Adv. 2022; 12: 34850–34873. 2046-2069. PubMed Abstract | Publisher Full Text | Free Full Text Reference Source

[7] 7. Shrivastav N, Madan J, Pandey R: Predicting photovoltaic efficiency in cs-based perovskite solar cells: A comprehensive study integrating scaps simulation and machine learning models. Solid State Commun. 2024; 380: 115437. 00381098. Publisher Full Text

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[9] 9. Elangovan NK, Kannadasan R, Beenarani BB, et al.: Recent developments in perovskite materials, fabrication techniques, band gap engineering, and the stability of perovskite solar cells. Energy Rep. 2024; 11: 1171–1190. 23524847. Publisher Full Text

[10] 10. Velez-Galvis Y, Sepulveda A, Echeverri-Perez J, et al.: Simulation of lead-free perovskite solar cell based on masni3 compared with fapbi3. Optica Latin America Optics and Photonics Conference (LAOP) 2024 (2024), paper Tu4A.16. 2024; page Tu4A.16. Publisher Full Text Reference Source

[11] 11. Stanić D, Kojić V, Bohač M, et al.: Simulation and optimization of fapbi3 perovskite solar cells with a batio3 layer for efficiency enhancement. Materials. 2022; 15. 19961944. PubMed Abstract | Publisher Full Text | Free Full Text

[12] 12. Assal S, Yadir S, Amiry H, et al.: Analysis of technological factors’s influence on solar cell’s performance by pc1d simulation. 2014 International Renewable and Sustainable Energy Conference (IRSEC). IEEE; 2014; pages 1–5. 978-1-4799-7336-1. Publisher Full Text Reference Source

[13] 13. Liu F, Zhu J, Wei J, et al.: Numerical simulation: Toward the design of high-efficiency planar perovskite solar cells. Appl. Phys. Lett. 2014; 104. 0003-6951. Publisher Full Text Reference Source

[14] 14. Alanazi TI, Eid OI, Okil M: Numerical study of flexible perovskite/si tandem solar cell using tcad simulation. Opt. Quant. Electron. 2023; 55: 1152. 0306-8919. Publisher Full Text

[15] 15. Deepthi Jayan K: Complete modelling and simulation of all perovskite tandem solar cells. Mater. Sci. Eng. B. 2023; 294: 116506. 09215107. Publisher Full Text Reference Source

[16] 16. Gong J: Simulation of steady-state characteristics of heterojunction perovskite solar cells in wxamps. Optik. 2021; 232: 166382. 00304026. Publisher Full Text Reference Source

[17] 17. Dadashbeik M, Fathi D, Eskandari M: Design and simulation of perovskite solar cells based on graphene and tio2/graphene nanocomposite as electron transport layer. Sol. Energy. 2020; 207: 917–924. 0038092X. Publisher Full Text Reference Source

[18] 18. Chabane H, Dehimi L, Bencherif H, et al.: Optimized al0.25ga0.75as solar cell performance using a new approach based on hybridizing silvaco tcad simulator with real coded genetic algorithm. J. Opt. 2024; pages 1–16. 0972-8821. Publisher Full Text

[19] 19. Jimoh OM, Ikechiamaka FN, Akinbolati A, et al.: Investigating the performance of perovskite solar cells using nickel oxide and copper iodide as ptype inorganic layers by scaps-1d simulation. Physics Access. 2022; 02: 37–50. 2756-3898. Publisher Full Text Reference Source

[20] 20. Abdelaziz S, Zekry A, Shaker A, et al.: Investigating the performance of formamidinium tin-based perovskite solar cell by scaps device simulation. Opt. Mater. 2020; 101: 109738. 09253467. Publisher Full Text Reference Source

[21] 21. Chabri I, Benhouria Y, Oubelkacem A, et al.: Scaps device simulation study of formamidinium tin-based perovskite solar cells: Investigating the influence of absorber parameters and transport layers on device performance. Sol. Energy. 2023; 262: 111846. 0038092X. Publisher Full Text Reference Source

[22] 22. Sanchez JEV, Londoño MAB, Sepulveda FAS, et al.: Absorber layer thickness as a new feature in statistical learning tools of perovskite solar cells. J. Appl. Res. Technol. 2023; 21: 858–865. 2448-6736. Publisher Full Text Reference Source

[23] 23. Zhan L, Ye D, Qiu X, et al.: Discovery of stable hybrid organic-inorganic double perovskites for high-performance solar cells via machine-learning algorithms and crystal graph convolution neural network method. arXiv. 2023. PubMed Abstract Reference Source

[24] 24. Azar MH, Aynehband S, Abdollahi H, et al.: Scaps empowered machine learning modelling of perovskite solar cells: Predictive design of active layer and hole transport materials. Photonics. 2023; 10: 271. 2304-6732. Publisher Full Text Reference Source

[25] 25. Rojas-Rincón C, Vélez-Galvis Y, Botero-Londoño M, et al.: Machine learning for the prediction of perovskite solar cell performance: A brief review. Congress on Research, Development and Innovation in Renewable Energies. Cham: Springer; 2025; pages 15–24. 978-3-031-88995-0. Publisher Full Text

[26] 26. Odabaşı Ç, Yıldırım R: Machine learning analysis on stability of perovskite solar cells. Sol. Energy Mater. Sol. Cells. 2020; 205: 110284. 09270248. Publisher Full Text Reference Source

[27] 27. Yan W, Liu Y, Zang Y, et al.: Machine learning enabled development of unexplored perovskite solar cells with high efficiency. Nano Energy. 2022; 99: 107394. 22112855. Publisher Full Text Reference Source

[28] 28. Yao L, Dong Wei W, Liu JM, et al.: Predicting the device performance of the perovskite solar cells from the experimental parameters through machine learning of existing experimental results. J. Energy Chem. 2023; 77: 200–208. 20954956. Publisher Full Text Reference Source

[29] 29. Mahmood A, Wang J-L: Machine learning for high performance organic solar cells: current scenario and future prospects. Energy Environ. Sci. 2021; 14: 90–105. 1754-5692. Publisher Full Text Reference Source

[30] 30. David TW, Anizelli H, Jacobsson TJ, et al.: Enhancing the stability of organic photovoltaics through machine learning. Nano Energy. 2020; 78: 105342. 22112855. Publisher Full Text Reference Source

[31] 31. Shimul AI, Khan M, Rayhan A, et al.: Machine learning-based optimization and performance enhancement of ch3nh3snbr3 perovskite solar cells with different charge transport materials using scaps-1d and wxamps. Advanced Theory and Simulations. 2025; 8. Publisher Full Text

[32] 32. Burgelman M: Scaps-1d: Solar cell capacitance simulator, 2005. Software for numerical simulation of photovoltaic devices.Reference Source

[33] 33. Ahmad S, Kazim S, Grätzel M: Perovskite Solar Cells. Wiley; 2021. 9783527347155. Publisher Full Text

[34] 34. Jacobsson TJ, Hultqvist A, García-Fernández A, et al.: An open-access database and analysis tool for perovskite solar cells based on the fair data principles. Nat. Energy. 2022; 7: 107–115. 20587546. Publisher Full Text

[35] 35. Benami A, Ouslimane T, Et-taya L, et al.: Comparison of the effects of zno and tio2 on the performance of perovskite solar cells via scaps-1d software package. J. Nano-Electron. Phys. 2022; 14: 01033-1–01033-5. 20776772. Publisher Full Text Reference Source

[36] 36. Homola T, Pospisil J, Shekargoftar M, et al.: Perovskite solar cells with low-cost tio2 mesoporous photoanodes prepared by rapid low-temperature (70 °c) plasma processing. ACS Appl Energy Mater. 2020; 3: 12009–12018. 2574-0962. Publisher Full Text

[37] 37. Afre RA, Pugliese D: Perovskite solar cells: A review of the latest advances in materials, fabrication techniques, and stability enhancement strategies.2024. 2072666X.

[38] 38. Zhang J, Zhang W, Cheng H-M, et al.: Critical review of recent progress of flexible perovskite solar cells. Mater. Today. 2020; 39: 66–88. 13697021. Publisher Full Text Reference Source

[39] 39. Devi C, Mehra R: Device simulation of lead-free masni3 solar cell with cusbs2 (copper antimony sulfide). J. Mater. Sci. 2020; 54: 5615–5624. 0022-2461. Publisher Full Text

[40] 40. Thakur D, Chiang S-E, Yang M-H, et al.: Self-stability of un-encapsulated polycrystalline mapbi3 solar cells via the formation of chemical bonds between c60 molecules and ma cations. Sol. Energy Mater. Sol. Cells. 2022; 235: 111454. 09270248. Publisher Full Text Reference Source

[41] 41. Shoab M, Aslam Z, Zulfequar M, et al.: Numerical interface optimization of lead-free perovskite solar cells (ch3nh3sni3) for 30 photo-conversion efficiency using scaps-1d. Next Materials. 2024; 4: 100200. 29498228. Publisher Full Text

[42] 42. Luo W, He J, Yu S, et al.: Optimizing the performance of tin-based perovskite solar cells employing 2d tungsten disulfide as an htl by numerical simulation. Phys. Status Solidi A. 2023; 220. 1862-6300. Publisher Full Text

[43] 43. Piñón AC, Reyes RC, Lázaro A, et al.: Study of a lead-free perovskite solar cell using czts as htl to achieve a 20. Micromachines. 2021; 12. 2072666X. PubMed Abstract | Publisher Full Text | Free Full Text

[44] 44. Banik S, Das A, Das BK, et al.: Numerical simulation and performance optimization of a lead-free inorganic perovskite solar cell using scaps-1d. Heliyon. 2024; 10: e23985. 24058440. PubMed Abstract | Publisher Full Text | Free Full Text Reference Source

[45] 45. COMSOL: Comsol multiphysics®.Reference Source

[46] 46. Kaur J, Sharma AK, Basu R, et al.: Simulation of planar heterojunction ch3nh3pbi3 solar cell employing sigesn alloy as a backplane. SILICON. 2024; 16: 1453–1466. 18769918. Publisher Full Text .

[47] 47. Izadi F, Ghobadi A, Gharaati A, et al.: Effect of interface defects on high efficient perovskite solar cells. Optik. 2021; 227: 166061. 00304026. Publisher Full Text

[48] 48. Ompong D, Clements M: Optimization of formamidinium-based perovskite solar cell using scaps-1d. Results in Optics. 2024; 14: 100611. 26669501. Publisher Full Text Reference Source

[49] 49. Bag A, Radhakrishnan R, Nekovei R, et al.: Effect of absorber layer, hole transport layer thicknesses, and its doping density on the performance of perovskite solar cells by device simulation. Sol. Energy. 2020; 196: 177–182. 0038092X. Publisher Full Text

[50] 50. Lee D, Kim K, Kim H-D: Thickness optimization of charge transport layers on perovskite solar cells for aerospace applications. Nanomaterials. 2023; 13: 1848. PubMed Abstract | Publisher Full Text | Free Full Text

[51] 51. Aseena S, Nelsa A, Suresh BV: Optimization of layer thickness of zno based perovskite solar cells using scaps 1d. Mater Today Proc. 2020; 43: 3432–3437. Publisher Full Text

[52] 52. Miah M, Mayeen Khandaker M, Rahman MA, et al.: Band gap tuning of perovskite solar cells for enhancing the efficiency and stability: issues and prospects. RSC Adv. 2024; 14: 15876–15906. PubMed Abstract | Publisher Full Text | Free Full Text

[53] 53. Yang Z, Surrente A, Gałkowski K, et al.: Unraveling the exciton binding energy and the dielectric constant in single crystal methylammonium lead triiodide perovskite. J. Phys. Chem. Lett. 2017; 8: 1851–1855. PubMed Abstract | Publisher Full Text

[54] 54. Rui S, Zhaojian X, Jiang W, et al.: Dielectric screening in perovskite photovoltaics. Nat. Commun. 2021; 12: 2479. PubMed Abstract | Publisher Full Text | Free Full Text

[55] 55. Sahoo A, Mohanty I, Mangal S: Effect of acceptor density, thickness and temperature on device performance for tin-based perovskite solar cell. Mater Today Proc. 2022; 62: 6210–6215. Publisher Full Text

[56] 56. Vaish S, Dixit SK: Study the effect of total defect density variation in absorbing layer on the power conversion efficiency of lead halide perovskite solar cell using scaps-1d simulation tool. Mater Today Proc. 2023; 91: 17–20. International Conference on Futuristic Materials. 2214-7853. Publisher Full Text Reference Source

[57] 57. Shockley W, Queisser HJ: Detailed balance limit of efficiency of p-n junction solar cells. J. Appl. Phys. 1961; 32(3): 510–519. 0021-8979. Publisher Full Text

[58] 58. National Renewable Energy Laboratory (NREL): Best research-cell efficiency chart.2025. Accessed 2025-08-01. Reference Source

[59] 59. Rahman MS, Ahmed N, Paul T, et al.: Performance evaluation of lead free ch3nh3sni3 perovskite solar cell: A simulation approach by scaps-1d. 2024 Third International Conference on Power, Control and Computing Technologies (ICPC2T). 2024; pages 392–397. Publisher Full Text

[60] 60. Mohammadi MH, Eskandari M, Fathi D: Design of optimized photonic-structure and analysis of adding a sio2 layer on the parallel ch3nh3pbi3/ch3nh3sni3 perovskite solar cells. Sci. Rep. 2023; 13: 15905. 20452322. PubMed Abstract | Publisher Full Text | Free Full Text

[61] 61. Ouslimane T, Et-taya L, Elmaimouni L, et al.: Impact of absorber layer thickness, defect density, and operating temperature on the performance of mapbi3 solar cells based on zno electron transporting material. Heliyon. 2021; 7: e06379. 24058440. PubMed Abstract | Publisher Full Text | Free Full Text Reference Source

[62] 62. Sharma AK, Kaushik DK: Numerical simulation of masni 3/cui heterojunction based perovskite solar cell. J. Phys. Conf. Ser. 2022; 2267: 012148. 1742-6588. Publisher Full Text .

[63] 63. Rana AD, Pharne ID, Bhargava K: Numerical simulation of highly efficient double perovskite solar cell using scaps-1d. Mater Today Proc. 2023; 73: 584–589. Nanomaterials for Energy Conversion and Storage Application-2022 (NECSA 2022). 2214-7853. Publisher Full Text Reference Source

[64] 64. Karthick S, Velumani S, Bouclé J: Experimental and scaps simulated formamidinium perovskite solar cells: A comparison of device performance. Sol. Energy. 2020; 205: 349–357. 0038092X. Publisher Full Text Reference Source

[65] 65. Minbashi M, Yazdani E: Comprehensive study of anomalous hysteresis behavior in perovskite-based solar cells. Sci. Rep. 2022; 12: 14916. 2045-2322. PubMed Abstract | Publisher Full Text | Free Full Text Reference Source

[66] 66. Stranks SD, Snaith HJ: Metal-halide perovskites for photovoltaic and light-emitting devices. Nat. Nanotechnol. 2015; 10: 391–402. Publisher Full Text

[67] 67. Jena AK, Kulkarni A, Miyasaka T: Halide perovskite photovoltaics: Background, status, and future prospects. Chem. Rev. 2019; 119: 3036–3103. PubMed Abstract | Publisher Full Text

[68] 68. 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] 69. Tress W: Metal halide perovskites as next-generation photovoltaic materials. Energy Environ. Sci. 2017; 10: 951–969.

[70] 70. Snaith HJ: Present status and future prospects of perovskite photovoltaics. Nat. Mater. 2018; 17: 372–376. PubMed Abstract | Publisher Full Text

[71] 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

Synthetic dataset to study the performance of perovskite solar cell simulations

Abstract

Keywords

1. Introduction

2. Methods

2.1 Device design

Figure 1. Perovskite solar cell structure.

2.2 Numerical modelling of PSC

(1)

(2)

(3)

(4)

(5)

(6)

(7)

2.3 Simulation data generation

Table 1. Variation ranges of geometric and physical parameters used for simulation.

2.4 Data validation

Table 2. Comparison between reported and simulated performance results for PSC.

3. Data description

Figure 2. Data distribution of the performance metrics: a) Voc, b) Jsc, c) FF, and d) PCE.

4. User notes

Author contributions

Data availability

Acknowledgements

References

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