Material science, at its core, is an interdisciplinary field focusing on the discovery and design of new materials. It combines elements of physics, chemistry and engineering to understand and ...

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 ...

A computational method combining generative AI with atomistic simulations can identify promising platinum alloy catalyst structures for hydrogen fuel cells, report researchers from Science Tokyo.

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 ...

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 ...

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 ...

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 ...

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 ...