LAUSR

Abstract The prediction of hydrogen release ability is indispensable to evaluating hydrogen storage performance of LiBH 4 -based mixtures before experimentation. To achieve this goal, ensemble machine learning is employed to automatically infer the relationship between factors (i.e., sample preparation, mixing conditions and operational variables) and target ( H 2 release amount), providing exceptional insight into hydrogen release ability. Specifically, the importance ranking of major variables for the hydrogen release of LiBH 4 has been proposed for the first time based on the constructed uni-component catalysts database. We train our developed EoE model on 2,071 uni-component catalysts data and attempt to predict the hydrogen release amounts of LiBH 4 doping with the unseen bi-component catalysts. The appealing results demonstrate the effectiveness and robustness of EoE. The procedure established in this study presents a novel approach for accelerating the research and development of hydrogen storage materials over various catalysts.