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

Materials with advanced customized properties drive innovation in a number of real-life applications across various fields, such as information technology, transportation, green energy and health ...

TCAD models are the fundamental building blocks for the semiconductor industry. Whether it is a new process node or a new multi-billion dollar fab, accurate TCAD models must be developed and ...

Some of the most encouraging results for reaction-enhancing catalysts come from one material in particular: tin (Sn). While Sn's overall utility as a catalyst is well-known, its underlying ...

While experimental screening of polymer libraries is time-consuming and costly, purely computational approaches have so far fallen short due to limited data availability and high computational demands ...

From 11.5 million alloy candidates to AI-guided perovskites, this piece unpacks how materials informatics is speeding up discovery, design, and deployment in engineering.

Brian Spears and colleagues built a generative machine learning model that was used to successfully predict the outcome of a recent fusion ignition experiment at the U.S. National Ignition Facility ...