The prediction of hydrogen release ability is indispensable to evaluating hydrogen storage performance of ${\text{LiBH}}_4$-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 (${\text{H}}_2$ release amount), providing exceptional insight into hydrogen release ability. Specifically, the importance ranking of major variables for the hydrogen release of ${\text{LiBH}}_4$ 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 ${\text{LiBH}}_4$ 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.

氢释放能力的预测对于评估氢的储氢性能必不可少。 ${\text{锂离子电池}}_4$实验之前使用基于混合物的混合物。为了实现这一目标，采用集成机器学习来自动推断因素（即样品制备，混合条件和操作变量）与目标之间的关系（${\text{H}}_{\mathrm{2个}}$释放量），提供对氢气释放能力的出色洞察力。具体来说，氢释放的主要变量的重要性等级${\text{锂离子电池}}_4$首次基于构建的单组分催化剂数据库提出了建议。我们在2,071个单组分催化剂数据上训练了我们开发的EoE模型，并试图预测氢的氢释放量${\text{锂离子电池}}_4$用看不见的双组分催化剂掺杂。诱人的结果证明了EoE的有效性和鲁棒性。在这项研究中建立的程序提出了一种新颖的方法，以加速在各种催化剂上储氢材料的研究和开发。