Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels
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 par...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Gels |
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| Online Access: | https://www.mdpi.com/2310-2861/11/1/70 |
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| author | Chengce Yuan Yimin Shi Zhichen Ba Daxin Liang Jing Wang Xiaorui Liu Yabei Xu Junreng Liu Hongbo Xu |
| author_facet | Chengce Yuan Yimin Shi Zhichen Ba Daxin Liang Jing Wang Xiaorui Liu Yabei Xu Junreng Liu Hongbo Xu |
| author_sort | Chengce Yuan |
| collection | DOAJ |
| description | 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<sup>2</sup> 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. |
| format | Article |
| id | doaj-art-fa3e15c3eef4473c951f29d6284fe751 |
| institution | Kabale University |
| issn | 2310-2861 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Gels |
| spelling | doaj-art-fa3e15c3eef4473c951f29d6284fe7512025-01-24T13:34:00ZengMDPI AGGels2310-28612025-01-011117010.3390/gels11010070Machine Learning Models for Predicting Thermal Properties of Radiative Cooling AerogelsChengce Yuan0Yimin Shi1Zhichen Ba2Daxin Liang3Jing Wang4Xiaorui Liu5Yabei Xu6Junreng Liu7Hongbo Xu8AVIC Shenyang Aircraft Corporation, Shenyang 110850, ChinaKey Laboratory of Bio-Based Material Science and Technology (Ministry of Education), Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Bio-Based Material Science and Technology (Ministry of Education), Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Bio-Based Material Science and Technology (Ministry of Education), Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Bio-Based Material Science and Technology (Ministry of Education), Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Bio-Based Material Science and Technology (Ministry of Education), Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Bio-Based Material Science and Technology (Ministry of Education), Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Bio-Based Material Science and Technology (Ministry of Education), Northeast Forestry University, Harbin 150040, ChinaSchool of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, ChinaThe 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<sup>2</sup> 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.https://www.mdpi.com/2310-2861/11/1/70radiative cooling aerogelsmachine learningSHAP analysis |
| spellingShingle | Chengce Yuan Yimin Shi Zhichen Ba Daxin Liang Jing Wang Xiaorui Liu Yabei Xu Junreng Liu Hongbo Xu Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels Gels radiative cooling aerogels machine learning SHAP analysis |
| title | Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels |
| title_full | Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels |
| title_fullStr | Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels |
| title_full_unstemmed | Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels |
| title_short | Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels |
| title_sort | machine learning models for predicting thermal properties of radiative cooling aerogels |
| topic | radiative cooling aerogels machine learning SHAP analysis |
| url | https://www.mdpi.com/2310-2861/11/1/70 |
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