Abstract Material informatics is a new initiative which has attracted a lot of attention in recent scientific research. The basic strategy is to construct comprehensive data sets and use machine learning to solve a wide variety of problems in material design and discovery. In pursuit of this goal, a key element is the quality and completeness of the databases used. Recent advance in the development of crystal structure prediction algorithms has made it a complementary and more efficient approach to explore the structure/phase space in materials using computers. In this talk, we discuss the importance of the structural motifs and motifnetworks in crystal structure predictions. Correspondingly, powerful methods are developed to improve the sampling of the low-energy structure landscape. Applications to the Li/Na-ion battery cathode materials, in particular $\text{A}_n\text{Fe}\text{Si}\text{O}_4$ ($n=1$ and $2$; $\text{A} = \text{Li}$ and $\text{Na}$) [1-5] and $\text{Li}\text{Fe}\text{P}\text{O}_4$ [6-7], will be presented.       Reference X. Zhao, et al. Sci. Rep. 5, 15555 (2015).  Z. Ye, et al. Electrochim. Acta 212, 934 (2016). S. Li, et al. ACS Appl. Mater. Interfaces 8, 17233 (2016). P. Wu, et al. Phys. Chem. Chem. Phys. 18, 23916 (2016).  X. Lv, et al. in preparation. Z. Zhu, et al. J. Phys. Chem. C 121, 11891 (2017). X. Lv, et al. J. Mater. Chem. A in press.     Biography Dr Kai-Ming Ho has completed his PhD from University of California, Berkeley in 1978. He is currently a Distinguished Professor in Liberal Arts and Sciences at Iowa State University and Fellow of American Physical Society since 1995.