Solid-state batteries (SSBs) are considered to be the next-generation energy storage devices. By replacing the flammable organic liquid electrolyte in conventional Li-ion batteries (LIBs) with an inorganic solid-state electrolyte (SSE), SSBs hold the promise to offer increased energy density, power density and safety compared with LIBs. However, two critical bottlenecks remain in the development of SSBs towards commercialization: the development of SSEs with ionic conductivities rivaling those of conventional liquid electrolytes, and the stabilization of interfaces between SSB components, including the active material, SSE and conductive additives.
Over the past sixty years, extensive experimental efforts have been devoted to the search of solid materials with high ionic conductivities at room temperature, which have led to discoveries of numerous classes of superionic conductors (SICs) that were employed as SSEs in solid-state batteries. For interface stabilization, the use of coating has been demonstrated to be an effective strategy in mitigating parasitic interface reactions in SSBs. Despite these advances, the low throughput of experimental trial-and-error approaches has bottlenecked the speed of new materials discovery for SSB development. On the other hand, thanks to the recent advances in first-principles calculations, the ability to accurately compute material properties in silico combined with large material databases has provided a computational approach to high-throughput screening of materials with the target functionality, revolutionizing the material discovery process.
In this dissertation, I focus on leveraging first-principles techniques based on density functional theory (DFT) to search for new SSEs and coating materials for SSB applications. Chapter 1 introduces the concept of solid-state batteries and recent advances in the studies of SSEs and interface stability. Chapter 2 is devoted to a computational search for lithium oxide SICs for SSE development. In this chapter, I first revisited the ion-conduction mechanisms in lithium garnet and NASICON (sodium superionic conductor) structures, the two most used oxide SSEs, based on their Li diffusion networks. Three network features that are beneficial for superionic conduction were identified, i.e., a 3D ion diffusion network, short distances between occupied Li sites, and “homogenous local environments.” Then, I performed a high-throughput screening to identify new lithium oxide conductors with these beneficial features. At the end of this search, I proposed 7 candidates as promising lithium oxide SICs with room-temperature ionic conductivity of ~0.1 mS/cm or higher predicted by first-principles calculations. Furthermore, several new structural frameworks emerged, including spinel, oxy-argyrodite, sodalite, and LixM(SeO3)2, opening up exciting opportunities for lithium oxide SSE development.
Chapter 3 employs a computational framework to evaluate and screen materials for cathode coatings in SSBs, focusing on their phase stability, electrochemical and chemical stability, and ionic conductivity. From this tiered screening, polyanionic oxide coatings were identified to exhibit optimal properties, with LiH2PO4, LiTi2(PO4)3, and LiPO3 being particularly appealing candidates. Some lithium borates exhibiting excellent (electro)chemical stability at various interfaces were also highlighted. These results demonstrated the promise of using optimized polyanionic materials as cathode coatings for SSBs.
Finally, Chapter 4 summarizes the main findings of the dissertation and provides an outlook for the future directions of SSB development.