Applying explainable artificial intelligence and ensemble boosting based machine learning models in predicting the thermal conductivity of nano enhanced phase change materials
Year: 2026
Type: article
Abstract: This study attempts the integration of explainable artificial intelligence and ensemble boosting based machine learning framework (XAI-EBBML-F) in predicting the thermal conductivity of nano enhanced phase change materials (NePCMs). The XAI-EBBML-F includes the following boosting based models: light gradient (LightGBM), extreme gradient (XGBoost), natural gradient (NGBoost) and categorical (CATBoost). For this, historical datasets (n = 510) are inferred from the selected three classifications of literature which specifically dealt with single-walled carbon nanotube (SWCNT), multi-walled carbon nanotube (MWCNT) and carbon nanofibers (CNF). From the inferred datasets, 70% of the datasets were used for training and 30% for testing purpose. For the supplied datasets, the NGBoost model in the XAI-EBBML-F recorded the best prediction performance (with R² = 0.9820, RMSE = 0.0692, and MAE = 0.0301 for training and R² = 0.9677, RMSE = 0.1208, and MAE = 0.0533 for testing). Also, the results of interpretability analysis identified concentration of NePCMs as the most influencing factor in deciding its thermal conductivity.
Source: Next Materials
Authors: G. Manikandan, S. Senthil Kumar, M. Sridharan
Institution: SRM Institute of Science and Technology
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,280
OpenAlex ID: W7162862657