Lithium-ion batteries, based on the pioneering work of three Nobel Laureates, are everywhere in our lives from portable electronics, electric vehicles, to grid storage. However, they currently employ liquid electrolytes containing flammable organic solvents that could lead to a fire if batteries are overheated. Solid electrolytes, also called fast-ion conductors or superionic conductors, are alternatives with the uttermost safety. Among various solid electrolytes, lithium garnet oxides are a promising family of materials due to their high ionic conductivity and electrochemical stability. This work discusses the study of diffusion and conduction in LixLa3Zrx-5Ta7-xO12 garnet oxides using computational methods. We developed two new generations of interatomic potentials, induced dipole, and machine learning, for this composition series. We compared them with existing interatomic potentials in terms of force/virial error against density-functional theory, prediction of phase transition, self-diffusivity, and ionic conductivity, and found machine learning interatomic potentials have the best accuracy. We then applied machine learning interatomic potentials to investigate the temperature and composition dependence of diffusion and conduction in bulk materials and the influence of grain boundary structure on ionic conductivity. We believe that the atomic insight obtained from this work could be worthwhile in understanding the bottleneck of materials performance and could provide guidance on further improvements.
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