Predictive modeling and design rules for solid electrolytes
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Introduction One of the most exciting prospects for energy storage is the development of solid-state batteries in which the flammable liquid electrolyte is replaced by a solid with extremely high Li-ion conductivity. The ability of computational modeling to accurately predict intrinsic properties of solid electrolytes has made modeling a critical component for understanding the behavior of solid electrolytes and their integration in solid-state batteries. In this article, we focus on the computational prediction of ionic conductivity, and electrochemical and interfacial stability with electrodes as the key properties for the stable operation of a solid electrolyte. The role of modeling in the development of solid-state conductors has been vital, as experiments often take time to converge on the true intrinsic behavior of a solid electrolyte due to complicating extrinsic effects on conductivity or difficulties in detecting interfacial reactivity. We briefly explain how the complex macroscopic requirements for a solid electrolyte can be turned into computable quantities and demonstrate the effectiveness of modeling in predicting novel conductors.
Design and prediction of novel solid electrolytes There are several complementary theoretical approaches to obtain information on the ionic conductivity of a crystalline
compound. These methods cover many orders of magnitudes of computational resource requirements, from simple topological analysis1 and empirical bond-valence approaches2 that can be run in seconds on a single core, to ab initio calculations3 that sometimes require more than tens of thousands of CPU hours to obtain converged ionic transport properties on a single material. Among the various computational methods, first-principles techniques based on density functional theory (DFT), such as ab initio molecular dynamics (AIMD) and nudged elastic band (NEB) calculations, have been widely adopted for the study of ionic transport in ionic conductors due to their high accuracy and transferability in different chemistries without any fitting parameters. The NEB method can be used to determine the activation barrier for an ion to migrate between two sites.4 Because of the exponential dependence of the conductivity on the migration barrier, it is the most basic quantity by which to separate fast ion conductors from poor conductors, with the best ion conductors having activation barriers under 200–300 meV. In fast ion conductors where carriers are usually freely available, the NEB migration barrier is often closely related to the measured activation energy for conductivity, once extrinsic conductivity limitations, such as interfacial and grain-boundary problems, have been overcome in experiments.
Gerbrand Ceder, University of California, Berkeley, USA; [email protected] Shyue Ping Ong, University of California, San Diego, USA; [email protected] Yan Wang, Advanced Materials Lab, Samsung Research America, USA; [email protected] doi:10.1557/mrs.2018.210
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• VOLUME 43 • OCTOBER 2018 • www.mrs.org/bulletin 2018
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