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 ...

Understanding the properties of different materials is an important step in material design. X-ray absorption spectroscopy (XAS) is an important technique for this, as it reveals detailed insights ...

Using the second-nearest neighboring atoms to predict metallic glass stability can help researchers more accurately model the disordered solid with strong, elastic properties, according to a recent ...

Kohei Noda, a researcher at JSR Corporation, and Professor Ryo Yoshida at the Institute of Statistical Mathematics, along with their research group, have developed an innovative machine learning ...

For more than a decade, a fundamental mystery has surrounded graphene—the one-atom-thick "wonder material" known for its ...

In joint research with the University of Tokyo (UTokyo), the National Institute of Advanced Industrial Science and Technology (AIST), Tohoku University, and Kyoto Institute of Technology, the National ...

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 ...

Join us to learn about how to use cutting edge GPU infrastructure to solve real world material discovery problems with AI and unsupervised machine learning. Our lab in the Department of Materials ...

Forbes contributors publish independent expert analyses and insights. Writes about the future of payments. We live in a world where machines can understand speech, recognize faces, and even generate ...