Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of ...

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle ...

Lithium-ion batteries have become the quiet workhorses of the energy transition, but the way they are designed and tested has ...

Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...

Understanding and predicting complex physical systems remain significant challenges in scientific research and engineering. Machine learning models, while powerful, often fail to follow the ...

Parisa Khodabakhshi is an assistant professor of mechanical engineering and mechanics in Lehigh University’s P.C. Rossin College of Engineering and Applied Science. Prior to joining the Lehigh faculty ...

Based on these challenges, a comprehensive reassessment of how AI should be deployed in electrocatalysis has become urgently needed. Addressing this need, a review published (DOI: 10.1016/j.esci.2025.

Muons tend to scatter more from high-atomic-number materials, so the technique is particularly sensitive to the presence of materials such as uranium. As a result, it has been used to create systems ...