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 ...

A recent study published in Small highlights how machine learning (ML) is reshaping the search for sustainable energy materials. Researchers introduced OptiMate, a graph attention network designed to ...

Hydrogen storage is limited by high pressure or cold tanks. Metal hydrides offer efficiency. A large curated database reveals key atomic traits to guide design. (Nanowerk News) Hydrogen fuels ...

(Nanowerk News) Researchers at the University of Toronto’s Faculty of Applied Science & Engineering have used machine learning to design nano-architected materials that have the strength of carbon ...

Two-dimensional (2D) materials have shown extraordinary potential in electrocatalytic reactions due to their unique structural and electronic properties. In a new review published in AI Mater., first ...

A team of researchers has successfully predicted abnormal grain growth in simulated polycrystalline materials for the first time -- a development that could lead to the creation of stronger, more ...

More aggressive feature scaling and increasingly complex transistor structures are driving a steady increase in process complexity, increasing the risk that a specified pattern may not be ...

NASA’s Transformational Tools and Technologies project integrates AI, advanced materials, and computational methods to ...