MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data ...

As semiconductor technologies advance, device structures are becoming increasingly complex. New materials and architectures introduce intricate physical effects requiring accurate modeling to ensure ...

The semiconductor industry is entering an era of unprecedented complexity, driven by advanced architectures such as Gate-All-Around (GAA) transistors, wide-bandgap materials like GaN and SiC, and ...

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 ...

A rotating cylinder with its side cut away to expose the core, showing patches of purple, blue, green, yellow, and orange that are dense in the middle and more diffuse toward the edges. This rotating ...

The integration of bioinformatics, machine learning and multi-omics has transformed soil science, providing powerful tools to unravel the complexity of ...

Researchers at Los Alamos National Laboratory have developed a new approach that addresses the limitations of generative AI models. Unlike generative diffusion models, the team's Discrete Spatial ...