This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …

The development of new batteries has historically been achieved through discovery and
development cycles based on the intuition of the researcher, followed by experimental trial …

Technologies that accelerate the delivery of reliable battery-based energy storage will not
only contribute to decarbonization such as transportation electrification, smart grid, but also …

All‐solid‐state lithium batteries have emerged as a priority candidate for the next generation
of safe and energy‐dense energy storage devices surpassing state‐of‐art lithium‐ion …

Recent years have seen monumental and exciting developments in the field of all-solid-state
batteries (ASSBs). Despite its promises, they still face a multitude of technical hurdles before …

As the world races to respond to the diverse and expanding demands for electrochemical
energy storage solutions, lithium‐ion batteries (LIBs) remain the most advanced technology …

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 …

Systems and methods are disclosed for communicating data in an integrated sensor network
having one or more nodes coupled to an optical sensor network and a radio frequency …

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 …

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 …