All-solid-state lithium ion batteries (ASSLBs) are considered next-generation devices for
energy storage due to their advantages in safety and potentially high energy density. As the …

Lithium‐ion batteries (LIBs) are vital energy‐storage devices in modern society. However,
the performance and cost are still not satisfactory in terms of energy density, power density …

For the proper design and evaluation of next‐generation lithium‐ion batteries, different
physical‐chemical scales have to be considered. Taking into account the electrochemical …

Batteries are of paramount importance for the energy storage, consumption, and
transportation in the current and future society. Recently machine learning (ML) has …

Emerging machine learning (ML) methods are widely applied in chemistry and materials
science studies and have led to a focus on data‐driven research. This Minireview …

Batteries are vital energy storage carriers in industry and in our daily life. There is continued
interest in the developments of batteries with excellent service performance and safety …

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 …

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 timely arrival of novel materials plays a key role in bringing advances to society, as the
pace at which major technological breakthroughs take place is usually dictated by the …

Predicting, monitoring, and optimizing the performance and health of a battery system
entails a variety of complex variables as well as unpredictability in given conditions. Data …