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Investigating the optoelectronic properties and photovoltaic performance of Na2AuGaBr6 based double perovskite solar cells via numerical simulation and AI techniques
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  • Published: 27 February 2026

Investigating the optoelectronic properties and photovoltaic performance of Na2AuGaBr6 based double perovskite solar cells via numerical simulation and AI techniques

  • Bipul Chandra Biswas1,
  • Asadul Islam Shimul1,
  • Indrojit Paul1,
  • S. AlFaify2,
  • Mohamed Benghanem3,
  • Md. Azizur Rahman4,
  • Gideon F. B. Solre5 &
  • …
  • Noureddine Elboughdiri6 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Energy science and technology
  • Materials science
  • Physics

Abstract

This study offers a thorough analysis of Density Functional Theory (DFT) and SCAPS-1D to assess the optoelectronic performance of cubic Na2AuGaBr6 double perovskites, highlighting its potential for advanced optoelectronic and photovoltaic applications. Device simulations were methodically conducted utilizing Na2AuGaBr6 as the active absorber material, in conjunction with various electron transport layers (ETLs) including TiO2, ZnO, WS2, C60, IGZO, and In2S3, as well as hole transport layers (HTLs) such as CuI, CFTS, NiO, CuSbS2, V2O5, Sb2S3, MoTe2, and CuO, to ascertain the optimal device configuration. Comprehensive parametric optimization was performed by analyzing the effects of different left (Co, Ni, Au, Pt, Pd, and Se) and right (Ca, Ba, Mg, Ag, Al, and Cr) metal contacts, band alignment, layer thicknesses, interface and bulk defect concentrations, as well as temperature on the overall photovoltaic performance. Of the 48 simulated device structures, the Al/FTO/WS2/Na2AuGaBr6/V2O5/Ni configuration demonstrated superior performance, achieving a power conversion efficiency (PCE) of 28.96%. Additionally, advanced machine learning (ML) and deep learning (DL) models were utilized to forecast and corroborate device performance trends. Of the eleven methods evaluated, the Gradient Boosting model exhibited remarkable predictive accuracy, attaining a R2 of 0.954 and a negligible mean absolute percentage error (MAPE) of 0.0218. These findings affirm the significant promise of Na2AuGaBr6-based perovskites for lead-free, high-efficiency solar systems and establish ML and DL-assisted modeling as an efficient method for performance improvement and material design in photovoltaic research.

Data Availability

The data that supports the findings of this study are available within the article and its Supporting data set and can be accessed at: https://tinyurl.com/32jw8v7w.

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Acknowledgements

The researchers wish to extend their sincere gratitude to the Deanship of Scientific Research at the Islamic University of Madinah (KSA) for the support provided to the Post-Publishing Program.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Gopalganj Science and Technology University, Gopalganj, 8105, Bangladesh

    Bipul Chandra Biswas, Asadul Islam Shimul & Indrojit Paul

  2. Department of Physics, College of Sciences, King Khalid University, P.O. Box 960, AlQura’a, Abha, Saudi Arabia

    S. AlFaify

  3. Physics Department, Faculty of Science, Islamic University of Madinah, Madinah, 42351, Saudi Arabia

    Mohamed Benghanem

  4. Department of Electrical and Electronic Engineering, Begum Rokeya University, Rangpur, 5400, Bangladesh

    Md. Azizur Rahman

  5. Department of Chemistry, Thomas J. R. Faulkner College of Science, Technology, Environment and Climate Change, University of Liberia, Monrovia, 00231, Montserrado County, Liberia

    Gideon F. B. Solre

  6. Chemical Engineering Department, College of Engineering, University of Ha’il, P.O. Box 2440, Ha’il, 81441, Saudi Arabia

    Noureddine Elboughdiri

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  1. Bipul Chandra Biswas
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Contributions

Bipul Chandra Biswas, Asadul Islam Shimul: Simulation, Formal analysis, Conceptualization, Resources, Data curation, data acquisition, Methodology, original manuscript writing, Manuscript editing and revision, and validation. Indrojit Paul: Simulation, Data curation, Formal analysis, Manuscript editing and revision. S. AlFaify, Mohamed Benghanem, Md. Azizur Rahman, Gideon F. B. Solre, Noureddine Elboughdiri: Formal analysis, Validation, Manuscript editing and revision.

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Correspondence to Asadul Islam Shimul, Mohamed Benghanem or Gideon F. B. Solre.

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Biswas, B.C., Shimul, A.I., Paul, I. et al. Investigating the optoelectronic properties and photovoltaic performance of Na2AuGaBr6 based double perovskite solar cells via numerical simulation and AI techniques. Sci Rep (2026). https://doi.org/10.1038/s41598-026-41519-x

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  • Received: 15 December 2025

  • Accepted: 20 February 2026

  • Published: 27 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-41519-x

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Keywords

  • Na2AuGaBr6
  • Double perovskite solar cell
  • Machine learning
  • Deep learning
  • SCAPS-1D
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