Some of the most encouraging results for reaction-enhancing catalysts come from one material in particular: tin (Sn). While ...

The infrastructure behind AI agents isn't static—it’s a living, evolving system. Designing effective data pipelines means ...

Semiconductor processing is notoriously challenging. It is one of the most intricate feats of modern engineering due to the ...

When someone starts a new job, early training may involve shadowing a more experienced worker and observing what they do ...

A machine learning approach leverages nuclear microreactor symmetry to reduce training time when modeling power output ...

Floods are some of the most devastating natural disasters communities in the United States face, causing billions of dollars ...

The new capabilities are designed to enable enterprises in regulated industries to securely build and refine machine learning ...

Researchers from The University of Texas at Austin used machine learning and artificial intelligence to develop new materials that could keep your house cooler and reduce energy bills.

AI's capacity to automate the grunt work of investment banking means humans can focus on the more valuable parts of the ...

In a first, Australian scientists turned to quantum machine learning to model semiconductor design, outperforming classical ...

Researchers at Tohoku University used machine learning potential to create large-scale models of tin (Sn) catalysts under ...

To realize business value from generative AI, start small, iterate fast and scale smart—while ensuring strong data, team ...