Li Chaoqun, Yang Weijie, Liu Hao, Liu Xinyuan, Xing Xiujing, Gao Zhengyang, Dong Shuai, Li Hao
School of Energy and Power Engineering, North China Electric Power University, Baoding, 071003, Hebei, China.
Chemistry Department, University of California, Davis, 95616, United States.
Angew Chem Int Ed Engl. 2024 Jul 8;63(28):e202320151. doi: 10.1002/anie.202320151. Epub 2024 Jun 7.
Developing solid-state hydrogen storage materials is as pressing as ever, which requires a comprehensive understanding of the dehydrogenation chemistry of a solid-state hydride. Transition state search and kinetics calculations are essential to understanding and designing high-performance solid-state hydrogen storage materials by filling in the knowledge gap that current experimental techniques cannot measure. However, the ab initio analysis of these processes is computationally expensive and time-consuming. Searching for descriptors to accurately predict the energy barrier is urgently needed, to accelerate the prediction of hydrogen storage material properties and identify the opportunities and challenges in this field. Herein, we develop a data-driven model to describe and predict the dehydrogenation barriers of a typical solid-state hydrogen storage material, magnesium hydride (MgH), based on the combination of the crystal Hamilton population orbital of Mg-H bond and the distance between atomic hydrogen. By deriving the distance energy ratio, this model elucidates the key chemistry of the reaction kinetics. All the parameters in this model can be directly calculated with significantly less computational cost than conventional transition state search, so that the dehydrogenation performance of hydrogen storage materials can be predicted efficiently. Finally, we found that this model leads to excellent agreement with typical experimental measurements reported to date and provides clear design guidelines on how to propel the performance of MgH closer to the target set by the United States Department of Energy (US-DOE).
开发固态储氢材料的需求一如既往地迫切，这需要全面了解固态氢化物的脱氢化学。过渡态搜索和动力学计算对于通过填补当前实验技术无法测量的知识空白来理解和设计高性能固态储氢材料至关重要。然而，对这些过程进行从头算分析在计算上既昂贵又耗时。迫切需要寻找能够准确预测能垒的描述符，以加速储氢材料性能的预测，并识别该领域的机遇和挑战。在此，我们基于Mg-H键的晶体哈密顿布居轨道与氢原子间距离的组合，开发了一个数据驱动模型来描述和预测典型固态储氢材料氢化镁（MgH）的脱氢能垒。通过推导距离能量比，该模型阐明了反应动力学的关键化学过程。该模型中的所有参数都可以直接计算，计算成本比传统的过渡态搜索显著降低，从而能够高效地预测储氢材料的脱氢性能。最后，我们发现该模型与迄今报道的典型实验测量结果高度吻合，并为如何推动MgH的性能更接近美国能源部（US-DOE）设定的目标提供了明确的设计指导。
