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<p>Halides perovskites of lead have already shown very good optoelectronic behavior, but because of their toxicity and environmental issues, lead-free materials have been sought faster. The paper investigates the performance of Rb<sub>2</sub>YAgX<sub>6</sub> (X = F, Cl) lead-free double perovskite photodetectors by means of a hybrid, first-principles and artificial intelligence (AI)-directed computational model. The stability of the structure, electronic band structure, density of states, and optical properties of Rb<sub>2</sub>YAgX<sub>6</sub> (X = F, Cl) were studied using Density Functional Theory (DFT). The analysis shows that Rb<sub>2</sub>YAgF<sub>6</sub> has a stable cubic structure and has a bandgap of about 2.1 to 2.3&#xa0;eV, which qualifies it to be used in the photodetection of the visible light. Simulations carried out by SETFOS also indicate that the performance of the device is highly dependent on the thickness of the absorbers, the concentration of doping, and the density of interface defects. Optical examination reveals the presence of intense absorption in the wavelength of 350 to 600&#xa0;nm with an absorption coefficient of about 10<sup>5</sup> cm<sup>− 1</sup>. In order to enhance the performance of the photodetector, AI-guided machine learning model was built on simulated data sets. Some of the device parameters that are considered during the optimization are the thickness of the absorber layer (200–600&#xa0;nm), carrier mobility (10–50 cm<sup>2</sup> V<sup>−1</sup>s<sup>− 1</sup>), carrier defect density (10<sup>13</sup>-10<sup>16</sup>&#xa0;cm<sup>− 1</sup>) and interface recombination velocity (10<sup>3</sup>-10<sup>5</sup>&#xa0;cm<sup>− 1</sup>). The trained model forecasts the effects of these parameters on such critical device parameters as responsivity, external quantum efficiency, and specific detectivity. The optimized design has a responsivity of about 0.38 AW<sup>− 1</sup>, an external quantum efficiency of over 75% and a detectivity of the order of 10<sup>12</sup> Jones. These findings reveal that AI-assisted optimization allows much better performance of a device at a lower cost of computation. The combined first principles and AI-based design approach offers a good route with which to arrive at high-performance and environment-friendly double perovskite photodetectors.</p>

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Artificial intelligence-guided and first-principles design and optimization of Rb₂YAgX₆ (X = F, Cl) double perovskite photodetectors

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  • 出版模式：

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  • 发表日期：

    2026年7月2日

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  • Mangey Ram Nagar,
  • Wasim Khan,
  • Niraj Agrawal,
  • Mohd. Sazid,
  • Saurabh Srivastava

摘要

Halides perovskites of lead have already shown very good optoelectronic behavior, but because of their toxicity and environmental issues, lead-free materials have been sought faster. The paper investigates the performance of Rb2YAgX6 (X = F, Cl) lead-free double perovskite photodetectors by means of a hybrid, first-principles and artificial intelligence (AI)-directed computational model. The stability of the structure, electronic band structure, density of states, and optical properties of Rb2YAgX6 (X = F, Cl) were studied using Density Functional Theory (DFT). The analysis shows that Rb2YAgF6 has a stable cubic structure and has a bandgap of about 2.1 to 2.3 eV, which qualifies it to be used in the photodetection of the visible light. Simulations carried out by SETFOS also indicate that the performance of the device is highly dependent on the thickness of the absorbers, the concentration of doping, and the density of interface defects. Optical examination reveals the presence of intense absorption in the wavelength of 350 to 600 nm with an absorption coefficient of about 105 cm− 1. In order to enhance the performance of the photodetector, AI-guided machine learning model was built on simulated data sets. Some of the device parameters that are considered during the optimization are the thickness of the absorber layer (200–600 nm), carrier mobility (10–50 cm2 V−1s− 1), carrier defect density (1013-1016 cm− 1) and interface recombination velocity (103-105 cm− 1). The trained model forecasts the effects of these parameters on such critical device parameters as responsivity, external quantum efficiency, and specific detectivity. The optimized design has a responsivity of about 0.38 AW− 1, an external quantum efficiency of over 75% and a detectivity of the order of 1012 Jones. These findings reveal that AI-assisted optimization allows much better performance of a device at a lower cost of computation. The combined first principles and AI-based design approach offers a good route with which to arrive at high-performance and environment-friendly double perovskite photodetectors.