Comparison of ion sites and diffusion paths in glasses obtained by molecular dynamics simulations and bond valence analysis
Christian Muller, Egbert Zienicke, Stefan Adams, Junko Habasaki, Philipp Maass
Abstract
Based on molecular dynamics simulations of a lithium metasilicate glass we study the potential of bond valence sum calculations to identify sites and diffusion pathways of mobile Li ions in a glassy silicate network. We find that the bond valence method is not well suitable to locate the sites, but allows one to estimate the number of sites. Spatial regions of the glass determined as accessible for the Li ions by the bond valence method can capture up to 90% of the diffusion path. These regions however entail a significant fraction that does not belong to the diffusion path. Because of this low specificity, care must be taken to determine the diffusive motion of particles in amorphous systems based on the bond valence method. The best identification of the diffusion path is achieved by using a modified valence mismatch in the BV analysis that takes into account that a Li ion favors equal partial valences to the neighboring oxygen ions. Using this modified valence mismatch it is possible to replace hard geometric constraints formerly applied in the BV method. Further investigations are necessary to better understand the relation between the complex structure of the host network and the ionic diffusion paths.
Create a lesson
Related papers
Temperature dependence of the charge density from first principles: application to the (222) forbidden reflection in silicon
Jean Paul Nery, Raveena Gupta, Olle Hellman et al.
Coupled anisotropic weak topological states and Floquet mixed-parity altermagnetism in two-dimensional Su-Schrieffer-Heeger models
Kunyuan Feng, Xibin Liu, Chenchen Liu et al.
Grain Boundary Phase Transitions Enable Diffusionless Climb of Disconnections
Md Sharier Nazim, Giacomo Po, Nikhil Chandra Admal
3D Cloud Component Analysis of Atomic Structures
Pai Li
Benchmarking of Fast and Interpretable UF Machine Learning Potentials
Pawan Prakash, Sam Dong, Richard G. Hennig
Grain-Boundary Premelting in High-Entropy Transition Metal Carbides
Marium M. Mou, Caleb Schenck, Samuel E. Daigle et al.