This Review presents an overview from the perspective of tetrahedral chemistry on various
oxide ion-conducting materials containing tetrahedral moieties which have received …

Abstract Machine learning (ML) exhibits substantial potential for predicting the properties of
solid‐state electrolytes (SSEs). By integrating experimental or/and simulation data within ML …

Solid‐state lithium batteries (SSLBs) have received considerable attention due to their
advantages in thermal stability, energy density, and safety. Solid electrolyte (SE) is a key …

With the development of artificial intelligence and the intersection of machine learning (ML)
and materials science, the reclamation of ML technology in the realm of lithium ion batteries …

Lithium-ion batteries (LIBs) are a promising energy storage system for green energy
applications. However, the use of liquid electrolytes in LIBs results in safety and lifespan …

Garnet solid-state electrolytes have become one of the most promising electrolyte materials
due to their high ionic conductivity, wide electrochemical window, and excellent …

Solid-state electrolytes have been demonstrated immense potential with their high density
and safety for Li, Na batteries. The discovery of novel crystals is of fundamental scientific and …

One compelling vision of the future of materials discovery and design involves the use of
machine learning (ML) models to predict materials properties and then rapidly find materials …

The widespread adoption of lithium-ion batteries has ushered in a transformative era across
industries, powering an array of devices from portable electronics to electric vehicles. This …

Nowadays, the safety concern for lithium batteries is mostly on the usage of flammable
electrolytes and the lithium dendrite formation. The emerging solid polymer electrolytes …