Recently, the team of Chen Lixin and Xiao Xuezhang from the School of Materials Science and Engineering of Zhejiang University cooperated with the team of Jiang Lijun and Li Zhinian. Published in the top international journal Energy Storage Materials entitled Machine Learning Enabled Customization of Performance-oriented Hydrogen Storage. Materials for Fuel Cell Systems research paper, Zhejiang University doctoral candidate Zhou Panpan as the first author of the paper. This study uses selected implicit/explicit eigenvalues to apply machine learning for the first time to the key microstructure of metal hydrides with a single C14-Laves structure and to target values such as hydrogen storage properties. By applying the optimized machine learning model to the alloy composition design of PEMFC fuel hydrogen supply system, the active performance scanning/prediction and subsequent alloy composition screening of specific parameters are successfully realized. The series of Ti-(Zr) -Mn-CR-VFe alloys customized by the above advanced form show superior comprehensive properties and competitive cost advantages compared with the best materials reported under the same pressure and temperature conditions. Based on the high performance hydrogen storage materials developed by the team, the fast response low pressure high density solid state hydrogen storage device developed by the team was successfully applied to Guangzhou Nansha electric hydrogen smart energy Station, helping China to realize the first time to connect fixed hydrogen power generation to the grid.