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 …

The increasing demand for high-security, high-performance, and low-cost energy storage
systems (EESs) driven by the adoption of renewable energy is gradually surpassing the …

All-solid-state batteries (ASSBs) have garnered considerable attention as promising
candidates for next-generation energy storage systems due to their potentially …

Aqueous zinc–iodine (Zn–I2) batteries, with their outstanding merits in safety, cost, and
environmental friendliness, have received extensive attention. However, the unstable …

The ceramic-based hybrid solid electrolytes (HSEs) represent excellent promise for
application in next generation all-solid-state lithium metal batteries (ASSBs) due to their fast …

Solid-state lithium batteries are on the threshold of commercialization as an alternative to
liquid electrolyte batteries. Glassy or amorphous solid electrolytes could bring crucial …

The widespread adoption of multimodal machine learning (ML) models such as GPT-4 and
Gemini has revolutionized various research domains, including computer vision and natural …

In the pursuit of high-performance solid-state batteries (SSBs), which can have excellent
safety and energy density, it is critical to understand the cyclability of emerging classes of …

Thioborate solid-state electrolytes are considered to be ideal solid-state electrolytes (SSEs)
due to their high theoretical ionic conductivity, wide electrochemical stability window …

Solid-state electrolytes (SSEs) are emerging as highly promising alternatives to
conventional organic liquid electrolytes due to their exceptional performance across a broad …