Iowa State University, USA
ScientificTracks Abstracts: Res. Rev. J Mat. Sci
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 motif-networks 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 AnFeSiO4 (n=1 and 2; A=Li and Na) and LiFePO4, will be presented. References 1.X Zhao, et al. (2015) Exploration of tetrahedral structures in silicate cathodes using a motif-network scheme. Sci. Rep.; 5: 15555. 2.S Li, et al. (2016) Zero-Strain Na2FeSiO4 as Novel Cathode Material for Sodium-Ion Batteries ACS Appl. Mater. Interfaces; 8(27): 17233-8. 3.P Wu, et al. (2016) Fe-Si networks in Na2FeSiO4 cathode materials. Phys. Chem. Chem. Phys; 18: 23916-22.
Kai-Ming Ho has completed his PhD from University of California, Berkeley. He is currently a Distinguished Professor in Liberal Arts and Sciences at Iowa State University and a Fellow of American Physical Society.