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

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 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 ...

MLIP calculations successfully identify suitable dopants for a novel photocatalytic material, report researchers from the ...

For his research in machine learning-based electron density prediction, Michigan Tech researcher Susanta Ghosh has been recognized with one of the National Science Foundation's highest honors. The NSF ...

Electron density prediction for a four-million-atom aluminum system using machine learning, deemed to be infeasible using traditional DFT method. × Researchers from Michigan Tech and the University of ...

The proton-conducting layer currently found in solid oxide fuel cells is typically made from a perovskite structure (left). Using machine learning, a research team, led by Kyushu University, has ...

Jason Rivas is researching materials at the atomic level to improve reliability and resistance of electronics to space radiation. A PhD student in materials science and engineering at the University ...

How can artificial intelligence (AI) machine learning models be used to identify new materials? This is what a recent study published in Nature hopes to address as a team of researchers investigated ...

Researchers have developed a framework that uses machine learning to accelerate the search for new proton-conducting materials, that could potentially improve the efficiency of hydrogen fuel cells.