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 ...

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 ...

The integration of machine learning techniques into microstructure design and the prediction of material properties has ushered in a transformative era for materials science. By leveraging advanced ...

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 ...

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 ...

Magnetic materials are in high demand. They're essential to the energy storage innovations on which electrification depends and to the robotics systems powering automation. They're also inside more ...

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 ...

Machine learning (ML) enables the accurate and efficient computation of fundamental electronic properties of binary and ternary oxide surfaces, as shown by scientists. Their ML-based model could be ...

The search for next-generation electronic materials often starts with studying the Fermi surface, which serves as a map of a ...

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 ...