Neel Somani has built a career that sits at the intersection of theory and practice. His work spans formal methods, mac ...

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 ...

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

Opinions expressed by Digital Journal contributors are their own. “In a world driven by data, my mission is to create innovative AI solutions that not only solve complex problems but also push the ...

Join us to learn about how to use cutting edge GPU infrastructure to solve real world material discovery problems with AI and unsupervised machine learning. Our lab in the Department of Materials ...

As generative artificial intelligence becomes a foundational layer in modern software and machine learning development, ...

In recent years, JupyterLab has rapidly become the tool of choice for data scientists, machine learning (ML) practitioners, and analysts worldwide. This powerful, web-based integrated development ...

With the aim of transforming the design of plastic materials, AIMPLAS, the Plastics Technology Center, has launched the POLY-ML project, an R&D initiative that applies advanced machine learning ...