The software tool developed by Stony Brook University uses self-supervised learning to detect long-term solar equipment damage weeks or years before manual inspections find it.

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, ...

A cross-institutional team led by researchers from the Department of Electrical and Electronic Engineering (EEE), under the ...

The process of testing new solar cell technologies has traditionally been slow and costly, requiring multiple steps. Led by a fifth-year PhD student, a Johns Hopkins team has developed a machine ...

In today's rapidly evolving technological world, marked by an incessant push for innovation and sustainability, Mr. Kannan Nova stands as a paragon of transformative change. With an impressive career ...

Researchers have developed a framework that uses machine learning to accelerate the search for new proton-conducting materials, that could potentially improve the efficiency of hydrogen fuel cells.

Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...