Screening of the alkali-metal ion containing materials from the Inorganic Crystal Structure Database (ICSD) for high ionic conductivity pathways using the bond valence method

Abstract High ionic conductivity is one of the key characteristics of electrolytes and electrode materials directly affecting performance of electrochemical devices in which they are used. In the case of inorganic crystalline solid electrolytes and insertion cathodes the topology and geometry of crystal structure essentially defines ionic conductivity and charge–discharge rates. We employed the bond valence method to identify materials with crystal structures featuring infinite networks of pathways of suitable size that is a prerequisite for fast ion transport. Taking advantage of the method low computational cost, we carried out exhaustive analysis of ~ 13,000 entries of the Inorganic Crystal Structure Database and ranked the materials based on the fraction of crystal structure space with low bond-valence mismatch. The results may be used as a guide for further theoretical and experimental studies of promising compositions.

[1]  M. Yashima.  Diffusion pathway of mobile ions and crystal structure of ionic and mixed conductors - A brief review , 2009 .

[2]  V. Urusov,et al.  Program for predicting interatomic distances in crystals by the bond valence method , 1998 .

[3]  M. Catti,et al.  Extensive lithium disorder in Li1.5Fe0.5Ti1.5(PO4)3 Nasicon by neutron diffraction, and the Li1+xFexTi2−x(PO4)3 phase diagram , 2004 .

[4]  Gerbrand Ceder,et al.  Opportunities and challenges for first-principles materials design and applications to Li battery materials , 2010 .

[5]  K. Kawamura,et al.  Structural disorder along the lithium diffusion pathway in cubically stabilized lithium manganese spinel II. Molecular dynamics calculation , 2003 .

[6]  D. Keen.  Disordering phenomena in superionic conductors , 2002 .

[7]  A. Yamada,et al.  Experimental visualization of lithium diffusion in LixFePO4. , 2008, Nature materials.

[8]  L. Pauling.  THE PRINCIPLES DETERMINING THE STRUCTURE OF COMPLEX IONIC CRYSTALS , 1929 .

[9]  Craig A. J. Fisher,et al.  Lithium Battery Materials LiMPO4 (M = Mn, Fe, Co, and Ni): Insights into Defect Association, Transport Mechanisms, and Doping Behavior , 2008 .

[10]  I. Brown,et al.  Recent Developments in the Methods and Applications of the Bond Valence Model , 2009, Chemical reviews.

[11]  J. Goodenough.  Challenges for Rechargeable Li Batteries , 2010 .

[12]  S. Adams,et al.  Pathway models for fast ion conductors by combination of bond valence and reverse Monte Carlo methods , 2002 .

[13]  Yukio Morii,et al.  Crystal Structure and Diffusion Path in the Fast Lithium-Ion Conductor La0.62Li0.16TiO3 , 2005 .

[14]  D. Mazza.  Modeling Ionic Conductivity in Nasicon Structures , 2001 .

[15]  Juan Rodríguez-Carvajal,et al.  Recent advances in magnetic structure determination by neutron powder diffraction , 1993 .

[16]  Tatsuji Numata,et al.  Verwey-Type Transition and Magnetic Properties of the LiMn2O4Spinels , 1997 .

[17]  P. Luksch,et al.  New developments in the Inorganic Crystal Structure Database (ICSD): accessibility in support of materials research and design. , 2002, Acta crystallographica. Section B, Structural science.

[18]  I. D. Brown,et al.  Bond‐valence parameters obtained from a systematic analysis of the Inorganic Crystal Structure Database , 1985 .

[19]  Adams,et al.  Determining ionic conductivity from structural models of fast ionic conductors , 2000, Physical review letters.