Yuan Chengce, Shi Yimin, Ba Zhichen, Liang Daxin, Wang Jing, Liu Xiaorui, Xu Yabei, Liu Junreng, Xu Hongbo
AVIC Shenyang Aircraft Corporation, Shenyang 110850, China.
Key Laboratory of Bio-Based Material Science and Technology (Ministry of Education), Northeast Forestry University, Harbin 150040, China.
Gels. 2025 Jan 16;11(1):70. doi: 10.3390/gels11010070.
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 R value of 0.943 and an RMSE of 1.423 for the test dataset. An interpretability analysis using Shapley additive explanations (SHAPs) identified a ZnO modifier (SHAP value, 1.523) and environmental parameters (ambient temperature, 1.299; solar irradiance, 0.979) as the most significant determinants of cooling performance. A feature interaction analysis further elucidated the complex interplay between the material composition and environmental conditions, providing theoretical guidance for material optimization.
不断升级的全球气候危机和能源挑战使得高效辐射冷却材料的开发变得日益紧迫。本研究提出了一种基于机器学习的模型，用于预测辐射冷却气凝胶（RCA）的性能。该模型整合了多个参数，包括材料组成（基体材料类型和比例）、改性设计（改性剂类型和含量）、光学性能（太阳反射率和红外发射率）以及环境因素（太阳辐照度和环境温度），以实现对冷却性能的准确预测。对各种机器学习算法的比较分析表明，优化后的XGBoost模型表现出卓越的预测性能，在测试数据集上的R值为0.943，均方根误差（RMSE）为1.423。使用Shapley加性解释（SHAP）进行的可解释性分析确定，ZnO改性剂（SHAP值为1.523）以及环境参数（环境温度，1.299；太阳辐照度，0.979）是冷却性能的最主要决定因素。特征交互分析进一步阐明了材料组成与环境条件之间的复杂相互作用，为材料优化提供了理论指导。
