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 ...
Tungsten's superior performance in extreme environments makes it a leading candidate for plasma-facing components (PFCs) in fusion reactors, but the ultra-high heat can damage its microscopic ...
Machine learning (ML) enables the accurate and efficient computation of fundamental electronic properties of binary and ternary oxide surfaces, as shown by scientists from Tokyo Tech. Their ML-based ...
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 ...
More aggressive feature scaling and increasingly complex transistor structures are driving a steady increase in process complexity, increasing the risk that a specified pattern may not be ...
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 ...
Programmable material systems are emerging architectural structures but the co-design of structure, material, and external stimuli present grand challenges. A team with Northwestern Engineering’s Wei ...
A computational method combining generative AI with atomistic simulations can identify promising platinum alloy catalyst ...
How additive manufacturing advanced the development of functionally graded materials. Why compositionally graded materials present a greater challenge to materials engineers. How computational ...
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 ...
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