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<p>Calcium bismuth fluoride (Ca<sub>3</sub>BiF<sub>3</sub>)-based hybrid perovskite solar cells (HPSCs) represent a promising lead-free alternative for next-generation photovoltaics, integrating advantageous semiconducting properties with the potential for cost-effective and environmentally sustainable energy conversion. This study presents an integrated computational framework that combines density functional theory (DFT), SCAPS-1D device modeling, and machine learning (ML) to enhance the design and optimization of Ca<sub>3</sub>BiF<sub>3</sub>-based solar cells. Intrinsic electronic and optical parameters derived from DFT informed SCAPS-1D simulations, which systematically investigated absorber thickness, defect density, doping concentration, and transport layer selection to analyze carrier dynamics, generation-recombination processes, and band alignment effects. Optimization demonstrated that P3HT-based HTL architecture (Al/FTO/TiO<sub>2</sub>/Ca<sub>3</sub>BiF<sub>3</sub>/P3HT/Ni) attains enhanced performance, achieving a power conversion efficiency (PCE) of 20.53%, a short-circuit current density (J<sub>SC</sub>) of 15.159&#xa0;mA/cm<sup>2</sup>, an open-circuit voltage (V<sub>OC</sub>) of 1.511&#xa0;V, and a fill factor (FF) of 89.60%, surpassing traditional CuI-based structures. The machine learning model developed using SCAPS data achieved a predictive accuracy of 95.2%, highlighting the significance of AI-driven tools in material evaluation and performance prediction. This work establishes a transparent pipeline that integrates quantum-level calculations, device simulations, and machine learning-guided predictions. It validates Ca<sub>3</sub>BiF<sub>3</sub> as a promising lead-free absorber and offers a scalable methodology for enhancing the development of efficient, stable, and sustainable thin-film solar cells for practical applications.</p>

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In-depth exploration of optoelectronic and PV characteristics in lead-free Ca3BiF3 perovskite solar cells: numerical simulations and machine learning approaches

  • 期刊论文

  • 发表日期：

    2025年10月28日

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  • Bipul Chandra Biswas,
  • Asadul Islam Shimul,
  • Abdulaziz A. Alshihri,
  • Ali El‑Rayyes,
  • Mohd Taukeer Khan,
  • Md. Azizur Rahman

摘要

Calcium bismuth fluoride (Ca3BiF3)-based hybrid perovskite solar cells (HPSCs) represent a promising lead-free alternative for next-generation photovoltaics, integrating advantageous semiconducting properties with the potential for cost-effective and environmentally sustainable energy conversion. This study presents an integrated computational framework that combines density functional theory (DFT), SCAPS-1D device modeling, and machine learning (ML) to enhance the design and optimization of Ca3BiF3-based solar cells. Intrinsic electronic and optical parameters derived from DFT informed SCAPS-1D simulations, which systematically investigated absorber thickness, defect density, doping concentration, and transport layer selection to analyze carrier dynamics, generation-recombination processes, and band alignment effects. Optimization demonstrated that P3HT-based HTL architecture (Al/FTO/TiO2/Ca3BiF3/P3HT/Ni) attains enhanced performance, achieving a power conversion efficiency (PCE) of 20.53%, a short-circuit current density (JSC) of 15.159 mA/cm2, an open-circuit voltage (VOC) of 1.511 V, and a fill factor (FF) of 89.60%, surpassing traditional CuI-based structures. The machine learning model developed using SCAPS data achieved a predictive accuracy of 95.2%, highlighting the significance of AI-driven tools in material evaluation and performance prediction. This work establishes a transparent pipeline that integrates quantum-level calculations, device simulations, and machine learning-guided predictions. It validates Ca3BiF3 as a promising lead-free absorber and offers a scalable methodology for enhancing the development of efficient, stable, and sustainable thin-film solar cells for practical applications.