Lithium-ion batteries have become the quiet workhorses of the energy transition, but the way they are designed and tested has ...

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

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

This Collection supports and amplifies research related to SDG 9 - Industry, Innovation & Infrastructure. Discovering new materials with customizable and optimized properties, driven either by ...

In a recent study in the Journal of Materials Chemistry A, a research team led by Professor Shinichi Komaba, Ms. Saaya Sekine, and Dr. Tomooki Hosaka from Tokyo University of Science (TUS), in ...

An agentic AI tool for battery researchers harnesses data from previous battery designs to predict the cycle life of new ...

Modeling-Driven Design of Materials and Electrodes for Beyond Lithium-Ion Batteries with Damla Eroglu, Department of Chem ...

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 ...

The field of additive manufacturing is undergoing a profound transformation as artificial intelligence (AI) and machine learning (ML) become integral to the ...

Literature searches, simulations, and practical experiments have been part of the materials science toolkit for decades, but the last few years have seen an explosion of machine learning-driven ...

National Institute of Standards and Technology effort uses machine learning to identify telltale sound of lithium-ion batteries hitting flash point. Two NIST researchers say they have trained a ...