Research authored by partners from the Bottle Consortium and published in Nature Communications this month aims to challenge ...
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 ...
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 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 ...
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 ...
Materials testing is critical in product development and manufacturing across various industries. It ensures that products can withstand tough conditions in their ...
How additive manufacturing advanced the development of functionally graded materials. Why compositionally graded materials present a greater challenge to materials engineers. How computational ...
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 ...
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