Gas sensing material screening faces challenges due to costly trial-and-error methods and the complexity of multi-parameter ...

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 ...

Dhruv Shenai investigates how machine learning and lab automation are transforming materials science at Cambridge ...

Researchers from China University of Petroleum (East China), in collaboration with international partners, have reported a ...

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 ...

Technologies that underpin modern society, such as smartphones and automobiles, rely on a diverse range of functional ...

Discover how a new machine learning method can help scientists predict which MOF structures are good candidates for advanced ...

Researchers from China University of Petroleum (East China), in collaboration with international partners, have reported a comprehensive review of ...

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 ...

Electro- and photocatalytic materials are central to enabling sustainable energy conversion processes such as water splitting, CO2 reduction, oxygen ...