The software tool uses self-supervised learning to detect long-term defects in solar assets weeks or years before ...

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 ...

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.

Frederick University and Electi Consulting Ltd have successfully completed the technical implementation of the DYNAMO research project, a decentralised energy management platform designed to allow ...

An astonishing 82 percent decrease in the cost of solar photovoltaic (PV) energy since 2010 has given the world a fighting chance to build a zero-emissions energy system which might be less costly ...

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 ...

Three decades ago, Yann LeCun, while at Bell Labs, formalized an approach to machine learning called convolutional neural networks that would prove to be profoundly productive in solving tasks such as ...

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

It's been said that engineers -- and some scientists, but mostly engineers -- can visualize in their mind's eye that which does not yet exist long before they sit down at the bench to construct ...