ArticleScientific reports2025
Enhanced prediction and optimization of thin metal film optical properties using optimized ensemble learning models.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Multi-Platform Software for Electrical and Microstructural Analysis of Silicon Solar Cell Metallization.Materials (Basel, Switzerland) · 2026Article
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5 authors.
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Abstract
Thin metal films are essential for expanding sensors, optoelectronic, and photovoltaic technologies. The intricate relationship between material composition, thickness, and production presents significant challenges in optimizing optical properties. The paper introduces an AI-driven framework for the simultaneous prediction and optimization of metal film optical characteristics, such as transmittance(%T), reflectance(%R), and absorptance(%A) using a dataset of 1320 experimentally measured samples across material films of gold, aluminum, nickel, tin, copper, and molybdenum over 200-2000 nm wavelength range. The input features to the model include wavelength and material type. Ensemble Models such as Random Forest, Gradient Boosting, XGBoost, and Extra Trees were trained and optimized through GridSearchCV with stratified K-fold cross-validation. A multitask learning model was also implemented to explore potential improvements from joint prediction. Among all models, the CatBoost Regressor demonstrated superior performance, achieving R
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