ArticleScientific reports2026
First principles and machine learning investigation of structural stability and optoelectronic behavior in A
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Physics-informed machine learning for lead-free dual-absorber perovskites: unveiling the synergy of CsRSC advances · 2026Article
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7 authors.
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Abstract
This research examines the structural, electronic, phonon, mechanical, and optical characteristics of alkali-based double perovskites A2GaAgF6 (where A = Na, K, Rb, and Cs) through Density Functional Theory (DFT) simulation. All compounds demonstrate both dynamic and mechanical stability, with direct band gaps between 1.5 and 2.7 eV, suggesting ductile behavior and anisotropy. To further evaluate device performance, One-dimensional Solar Cell Capacitance Simulator (SCAPS-1D) simulations were performed. A total of 196 device architectures were investigated by integrating seven electron transport layers (ETLs: C60, In2S3, CdS, ZnO, IGZO, SnS2, and MZO) with seven-hole transport layers (HTLs: CFTS, CuI, Sb2S3, PTAA, CuSbS2, MoO3, and MoTe2). Among these configurations, four structures were examined in detail. The Al/FTO/In2S3/Na2GaAgF6/PTAA/Ni device demonstrates highest power conversion efficiency (PCE) of 28.87%, accompanied by a short-circuit current density (JSC) of 25.55 mA/cm2, a fill factor (FF) of 89.92%, and an open-circuit voltage (VOC) of 1.256 V. Devices utilizing K2GaAgF6, Rb2GaAgF6, and Cs2GaAgF6 demonstrated PCEs of 14.89%, 12.38%, and 7.09% respectively. The effects of contact materials, band alignment, absorber thickness, defect density, doping concentration, and interface defect characteristics were analyzed systematically. To accelerate device optimization, machine learning based on the Random Forest algorithm was developed. Analysis of feature importance indicated that the absorber band gap and defect density are the primary factors influencing device efficiency. The model exhibited high predictive accuracy, with Root Mean Square Error (RMSE) of 0.177 and coefficient of determination (R2) of 0.972, indicating strong alignment with SCAPS-1D simulation results. This integrated framework provides a reliable and effective method for designing lead-free, sustainable, and high-performance double perovskite solar cells.
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