Picturing the Gap Between the Performance and US‐DOE's Hydrogen Storage Target: A Data‐Driven Model for MgH2 Dehydrogenation

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