Ensemble Classification of Hydrogen Storage Materials Using Its Properties

This research study analyzes the complex ensemble models with a specific focus on the Bagging Tree algorithm, Random Forest, and Ensemble Extra Tree. The objective of the proposed research work is to ascertain the effectiveness of these models in classifying the hydrogen storage materials. This research study relies on a comprehensive dataset that encompasses crucial properties, including hydrogen weight percent, heat of formation (measured in kJ per mol H2), temperature (in °C), pressure (in atmospheres absolute), entropy of formation (expressed in J per mol H2 per K), equilibrium pressure at 25 °C, natural logarithm of equilibrium pressure at 25 °C, and hydrogen-to-metal ratio. The combination of these features serves as a foundation for constructing robust classification models. Through the utilization of the collective predictive capabilities of these ensemble techniques, this study aims to enhance the precision and reliability of models by ultimately contributing to the forefront of materials science and the advancement of technologies in the realm of renewable energy.