Accelerated Discovery of Novel Garnet-Type Solid-State Electrolyte Candidates via Machine Learning

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 Li7La3Zr2O12 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.