Lithium-ion batteries have become the quiet workhorses of the energy transition, but the way they are designed and tested has long been slow, expensive, and heavily empirical. Machine learning is now ...

Korea University researchers have developed a machine-learning framework that predicts solar cell efficiency from wafer quality, enabling early wafer screening and optimized production paths. Using ...

Chao Hu is at the School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut, Storrs, Connecticut 06269, USA. The second category, data-driven lifetime prediction, uses ...

Researchers claim model can cut years from testing cycles Scientists have developed a machine learning method that could ...

Alfred University’s Inamori School of Engineering recently hosted a short course on battery machine learning, which was attended by a group of students and representatives of a Binghamton-area company ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

Digital twins and prognostic models deliver detailed insights into a battery’s behaviour and lifespan, and machine learning..

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...

Unlike traditional testing, which requires hundreds or thousands of charge – discharge cycles, the model can estimate a new ...

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 ...

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...