article · Next Materials
Simulated designs for ecofriendly thin-film solar cells demonstrate that zinc-doped copper oxide functions effectively as a low-cost absorber material. By combining SCAPS-1D device modelling with machine learning, the investigation assessed cell configurations incorporating tungsten disulfide as an electron transport layer and copper iodide as a hole transport layer. Evaluating six machine-learning algorithms identified Random Forest and Gradient Boosting as the most accurate predictors of performance. Analysis established zinc doping concentration as the primary driver of device efficiency, contributing 72 per cent of the overall effect, with optimal performance occurring at six to eight per cent doping. Under standard test conditions, the modelled architecture achieved a power conversion efficiency of 25.88 per cent, which increased to 26.45 per cent following tuning of the tungsten disulfide layer.
Developing efficient, non-toxic, and affordable solar cell materials is essential for accelerating clean energy adoption. By pairing computational device simulation with machine learning, researchers can rapidly evaluate and refine material combinations, reducing the time and cost required to discover viable thin-film photovoltaic materials that do not rely on expensive or environmentally harmful components.
This work could assist thin-film photovoltaic manufacturers and materials developers seeking lower-cost, ecofriendly absorber alternatives to conventional materials. Because the findings are based entirely on computational simulations and machine-learning predictions rather than physical device fabrication, the technology is at an early research stage. Experimental laboratory synthesis and physical testing are needed to validate the predicted performance before any industrial application can proceed.
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Addressing the critical demand for high-efficiency, thermally stable, and low-cost photovoltaics, this highly impactful work presents a breakthrough solar cell architecture featuring WS 2 as the electron transport layer and CuI as the hole transport layer. We rigorously analyzed WS 2 /CuO and CuO/CuI interfaces to optimize charge transport using SCAPS-1D simulations. To accelerate parameter exploration, six machine-learning models were evaluated, with Random Forest and Gradient Boosting achieving superior predictive accuracy (MAE <1%, R 2 =0.90). Feature importance analysis revealed zinc doping concentration as the dominant performance driver (72% contribution), with efficiency declining beyond 6–8% doping. Under standard conditions (300 K), the optimized device achieved a remarkable PCE of 25.88% (Jsc=20.10 mA/cm², Voc=1.447 V, FF=89.05%), further rising to 26.45% with WS 2 layer tuning. These results provide insights into the relationship between Zn concentration, defect density, interface quality, and photovoltaic performance, highlighting 8% Zn-doped CuO/ WS2 as a promising and potentially low-cost absorber material for thin-film solar-cell applications.
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DOI: 10.1016/j.nxmate.2026.103396
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