Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels.

ABSTRACT: The escalating global climate crisis and energy challenges have made the development of efficient radiative cooling materials increasingly urgent. This study presents a machine-learning-based model for predicting the performance of radiative cooling aerogels (RCAs). The model integrated multiple parameters, including the material composition (matrix material type and proportions), modification design (modifier type and content), optical properties (solar reflectance and infrared emissivity), and environmental factors (solar irradiance and ambient temperature) to achieve accurate cooling performance predictions. A comparative analysis of various machine learning algorithms revealed that an optimized XGBoost model demonstrated superior predictive performance, achieving an R² value

SUBMITTER: Yuan C 

PROVIDER: S-EPMC11765191 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature