Categories: AI/ML News

‘Discovery learning’ AI tool predicts battery cycle life with just a few days’ data

An agentic AI tool for battery researchers harnesses data from previous battery designs to predict the cycle life of new battery concepts. With information from just 50 cycles, the tool—developed at University of Michigan Engineering—can predict how many charge-discharge cycles the battery can undergo before its capacity drops below 90% of its design capacity.

Integrated modeling approach decodes solid-state battery microstructures for better performance

Researchers at Lawrence Livermore National Laboratory (LLNL) have developed a novel, integrated modeling approach to identify and improve key interface and microstructural features in complex materials typically used for advanced batteries. The work helped unravel the relationship between material microstructure and key properties and better predict how those properties affect…

January 31, 2025

In "AI/ML News"

AI prescribes new electrolyte additive combinations for enhanced battery performance

Batteries, like humans, require medicine to function at their best. In battery technology, this medicine comes in the form of electrolyte additives, which enhance performance by forming stable interfaces, lowering resistance and boosting energy capacity, resulting in improved efficiency and longevity.

August 28, 2025

In "AI/ML News"

Study employs image-recognition AI to determine battery composition and conditions

An international collaborative research team has developed an image recognition technology that can accurately determine the elemental composition and the number of charge and discharge cycles of a battery by examining only its surface morphology using AI learning.

July 3, 2024

In "AI/ML News"

AI Generated Robotic Content