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<p>The transition to sustainable energy sources has heightened interest in perovskite solar cells (PSCs), yet commercialization remains challenged by the toxicity of lead-based compounds and their vulnerability to environmental degradation. In response, this study presents a comprehensive computational framework for designing and optimizing a lead-free, inorganic PSC that employs Ba<sub>3</sub>SbI<sub>3</sub> as the absorber material. Implementing SCAPS-1D simulations, we systematically evaluate the influence of device architecture, carrier transport layers (CTLs), defect densities, doping concentrations, and operational conditions on key photovoltaic metrics. When different device layouts were compared, the Al/FTO/SnS<sub>2</sub>/Ba<sub>3</sub>SbI<sub>3</sub>/CBTS/Au structure stood out as the most promising. It yielded a simulated power conversion efficiency (PCE) of 33.31%, accompanied by an open-circuit voltage (<i>V</i><sub>oc</sub>) of 1.2068 V, short-circuit current density (<i>J</i><sub>sc</sub>) of 32.17 mA/cm<sup>2</sup> and fill factor (FF) of 85.81%, outperforming the reference model without a hole transport layer (HTL) achieved PCE = 21.55%, <i>V</i><sub>oc</sub> = 0.9089 V, <i>J</i><sub>sc</sub> = 27.83 mA/cm<sup>2</sup>, FF = 85.19%. Beyond parametric optimization, we incorporate machine learning (ML) via Random Forest Regression (RFR) to enhance predictive modeling capabilities. The model demonstrates high fidelity (<i>R</i>² &gt; 0.97) and offers insight into variable importance, identifying absorber defect density, doping level, and operational temperature as primary performance drivers. Our hybrid approach not only accelerates the design of high-efficiency Ba<sub>3</sub>SbI<sub>3</sub>-based PSCs but also underscores the viability of environmentally benign materials for next-generation photovoltaics. The integration of physics-based simulation with data-driven analysis provides a scalable methodology for future studies in sustainable solar energy technology.</p> Graphical Abstract <p></p>

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ML-integrated predictive modeling and performance evaluation of inorganic Ba3SbI3 perovskite solar cell using SCAPS-1D

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月24日

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  • Md. Anwer Hossain,
  • Md Rasidul Islam,
  • Naseem Akhter,
  • Sobhi M. Gomha,
  • Magdi E. A. Zaki,
  • Md Masud Rana

摘要

The transition to sustainable energy sources has heightened interest in perovskite solar cells (PSCs), yet commercialization remains challenged by the toxicity of lead-based compounds and their vulnerability to environmental degradation. In response, this study presents a comprehensive computational framework for designing and optimizing a lead-free, inorganic PSC that employs Ba3SbI3 as the absorber material. Implementing SCAPS-1D simulations, we systematically evaluate the influence of device architecture, carrier transport layers (CTLs), defect densities, doping concentrations, and operational conditions on key photovoltaic metrics. When different device layouts were compared, the Al/FTO/SnS2/Ba3SbI3/CBTS/Au structure stood out as the most promising. It yielded a simulated power conversion efficiency (PCE) of 33.31%, accompanied by an open-circuit voltage (Voc) of 1.2068 V, short-circuit current density (Jsc) of 32.17 mA/cm2 and fill factor (FF) of 85.81%, outperforming the reference model without a hole transport layer (HTL) achieved PCE = 21.55%, Voc = 0.9089 V, Jsc = 27.83 mA/cm2, FF = 85.19%. Beyond parametric optimization, we incorporate machine learning (ML) via Random Forest Regression (RFR) to enhance predictive modeling capabilities. The model demonstrates high fidelity (R² > 0.97) and offers insight into variable importance, identifying absorber defect density, doping level, and operational temperature as primary performance drivers. Our hybrid approach not only accelerates the design of high-efficiency Ba3SbI3-based PSCs but also underscores the viability of environmentally benign materials for next-generation photovoltaics. The integration of physics-based simulation with data-driven analysis provides a scalable methodology for future studies in sustainable solar energy technology.

Graphical Abstract