All-solid-state batteries (ASSBs) have attracted considerable attention because of their higher energy density and stability than conventional lithium-ion batteries (LIBs). For the development of promising ASSBs, solid-state electrolytes (SSEs) are essential to achieve structural integrity. Thus, in this study, a machine-learning-based surrogate model was developed to search for ideal garnet-type SSE candidates. The well-known Li 7 La 3 Zr 2 O 12 structure was used as a base material, and 73 chemical elements were substituted on La and Zr sites, leading to 5329 potential structures. First, the elasticity database and machine learning descriptors were adopted from previous studies. Subsequently, the machine-learning-based surrogate model was applied to predict the elastic properties of potential SSE materials, followed by first-principles calculations for validation. Furthermore, the active learning process demonstrated that it can effectively decrease prediction uncertainty. Finally, the ionic conductivity of the mechanically superior materials was predicted to suggest optimal SSE candidates. Then, ab initio molecular dynamics simulations are followed for confirmation of diffusion behavior for materials classified as superionic; 10 new tetragonal-phase garnet SSEs are verified with superior mechanical and ionic conductivity properties. We believe that the current model and the constructed database will become a cornerstone for the development of next-generation SSE materials.

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Na-ion batteries are considered a promising alternative to the analogous Li-ion batteries because of their low manufacturing cost, large abundance, and similar chemical/electrochemical properties. In particular, research on Na-ion solid electrolytes, which resolve the flammability issues associated with liquid electrolytes and increase the energy density obtained using a particular metal anode, is rapidly growing. However, the ionic conductivities of these materials are lower than those of liquids. We present a novel classification approach based on machine learning for identifying Na superionic conductor (NASICON) materials with outstanding ionic conductivities. We obtained new features based on chemical descriptors such as Na content, elemental radii, and electronegativity. We then classified 3573 NASICON structures by implementing the ensemble model of gradient boosting algorithms, with an average prediction accuracy of 84.2%. We further validated the thermodynamic stability and ionic conductivity values of the materials classified as superionic materials by employing density functional theory calculations and ab initio molecular dynamics simulations.

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