Predicting effective thermal conductivity of HGM composite using ML

Data-driven materials research can be used as an effective tool to supplement conventional approaches in designing new materials. Hollow glass microsphere (HGM) composites are widely used for high-temperature thermal insulation applications, and their synthesis is traditionally done by varying parameters of the constituents on a trial-and-error basis. In this work, a prediction tool based on a supervised machine learning (ML) model is developed to predict the effective thermal conductivity (ETC) of an HGM composite by tailoring the composition and parameters of the constituents to reduce the time and cost involved. A comprehensive database containing the various input features is generated from previous experimental investigations conducted on various HGM composites. ML models, namely random forest regression (RF), K-nearest neighbor (KNN), support vector regression (SVR), and artificial neural network (ANN), are used to predict the ETC of HGM composites. Feature importance analysis showed that the matrix material's thermal conductivity and the composite's bulk density have the highest impact on the ETC of the HGM composite, followed by porosity and average microsphere size. ANN emerged as the best model to predict HGM composite ETC with the lowest root mean square error and highest R-2 value for predictions. Moreover, a systematic approach for key feature selection using ANN shows that adding or omitting additional features beyond an optimal combination degrades the model's predictive accuracy.