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 ...
(Nanowerk News) Researchers at the University of Toronto’s Faculty of Applied Science & Engineering have used machine learning to design nano-architected materials that have the strength of carbon ...
A research team led by Chang Keke from the Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy ...
Scientists have used artificial intelligence (AI) to design never-before-seen nanomaterials with the strength of carbon steel and the lightness of styrofoam. The new nanomaterials, made using machine ...
Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...
Machine learning tools can accelerate all stages of materials discovery, from initial screening to process development.
Materials testing is critical in product development and manufacturing across various industries. It ensures that products can withstand tough conditions in their ...
From 11.5 million alloy candidates to AI-guided perovskites, this piece unpacks how materials informatics is speeding up ...
There is more than one way to describe a water molecule, especially when communicating with a machine learning (ML) model, says chemist Robert DiStasio. You can feed the algorithm the molecule's ...
In my previous blog, I reached out to this community of engineers to gauge the experiences and expectations you have regarding artificial intelligence (AI) at work. The feedback was interesting and ...
When a quantum computer processes data, it must translate it into understandable quantum data. Algorithms that carry out this 'quantum compilation' typically optimize one target at a time. However, a ...
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