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<p>Researchers worldwide are actively exploring novel materials for clean and sustainable energy generation. Among these, lead-free double perovskites have emerged as particularly promising candidates due to their strong light absorption and tunable electronic properties. Advanced computational methods now enable the precise prediction and understanding of these materials’ properties through highly accurate theoretical models. In this research work, the WIEN2k software package is employed to investigate the electronic structure of chloride, Cs<sub>2</sub>AgXCl<sub>6</sub> (X = In, Bi), double perovskites for energy harvesting applications. High-precision calculations were done with the WIEN2k, full potential linearized augmented plane wave (FP-LAPW), code. The strongly constrained and appropriately normed (SCAN), a meta-GGA functional, predicted lattice constants of 10.5018 and 10.7818 (Å) in close agreement with the experimental values of 10.470 and 10.7774 (Å) InCl<sub>6</sub> and BiCl<sub>6</sub> respectively. Despite this, it produced significantly lower electronic band gaps. To correct this underestimation, the Tran-Blaha modified Becke-Johnson (TB-mBJ) potential was applied in combination with SCAN, yielding improved band gap values for chlorides (3.087 /2.873&#xa0;eV, InCl<sub>6</sub>/ BiCl<sub>6</sub>). These results align well with experimental data (3.23/2.77&#xa0;eV InCl<sub>6</sub>/ BiCl<sub>6</sub>) verifying the effectiveness of the SCAN + TB-mBJ combination in accurately predicting the optoelectronic behavior of lead-free halide double perovskites. Goldschmidt tolerance factors and elastic constants were used to check the stability of these materials. The optical and photocatalytic activities show that bismuth-based double perovskites have comparatively better absorption in the visible region than indium-based double perovskites. Machine learning was further employed for rapid band-gap screening using eleven regression models. An ensemble of the five best-performing models, trained on TB-mBJ band-gap data, was subsequently applied to the two chlorides, predicting band gaps of (2.84 ± 0.19) eV for Cs<sub>2</sub>AgInCl<sub>6</sub> and (2.46 ± 0.04) eV for Cs<sub>2</sub>AgBiCl<sub>6</sub>. These values are in reasonable agreement with the corresponding SCAN + mBJ and experimental results of 3.087/2.873&#xa0;eV and 3.23/2.77&#xa0;eV, respectively. This approach may facilitate the rapid screening and future discovery of new double-perovskite materials.</p>

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First-principles optoelectronic analysis and machine-learning-assisted band-gap screening of Cs2AgXCl6 (X = In, Bi) double perovskites for energy-harvesting applications

  • 期刊论文

  • 发表日期：

    2026年7月17日

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  • Ghiyas Anwar,
  • Zafar Iqbal,
  • Muhammad Majid Gulzar,
  • Hamid Ullah

摘要

Researchers worldwide are actively exploring novel materials for clean and sustainable energy generation. Among these, lead-free double perovskites have emerged as particularly promising candidates due to their strong light absorption and tunable electronic properties. Advanced computational methods now enable the precise prediction and understanding of these materials’ properties through highly accurate theoretical models. In this research work, the WIEN2k software package is employed to investigate the electronic structure of chloride, Cs2AgXCl6 (X = In, Bi), double perovskites for energy harvesting applications. High-precision calculations were done with the WIEN2k, full potential linearized augmented plane wave (FP-LAPW), code. The strongly constrained and appropriately normed (SCAN), a meta-GGA functional, predicted lattice constants of 10.5018 and 10.7818 (Å) in close agreement with the experimental values of 10.470 and 10.7774 (Å) InCl6 and BiCl6 respectively. Despite this, it produced significantly lower electronic band gaps. To correct this underestimation, the Tran-Blaha modified Becke-Johnson (TB-mBJ) potential was applied in combination with SCAN, yielding improved band gap values for chlorides (3.087 /2.873 eV, InCl6/ BiCl6). These results align well with experimental data (3.23/2.77 eV InCl6/ BiCl6) verifying the effectiveness of the SCAN + TB-mBJ combination in accurately predicting the optoelectronic behavior of lead-free halide double perovskites. Goldschmidt tolerance factors and elastic constants were used to check the stability of these materials. The optical and photocatalytic activities show that bismuth-based double perovskites have comparatively better absorption in the visible region than indium-based double perovskites. Machine learning was further employed for rapid band-gap screening using eleven regression models. An ensemble of the five best-performing models, trained on TB-mBJ band-gap data, was subsequently applied to the two chlorides, predicting band gaps of (2.84 ± 0.19) eV for Cs2AgInCl6 and (2.46 ± 0.04) eV for Cs2AgBiCl6. These values are in reasonable agreement with the corresponding SCAN + mBJ and experimental results of 3.087/2.873 eV and 3.23/2.77 eV, respectively. This approach may facilitate the rapid screening and future discovery of new double-perovskite materials.