Mystery of how important aluminum minerals form and dissolve solved with materials science and advanced imaging studies.

Three PNNL technologies have been named 2024 R&D 100 Award winners.

Complex metal alloys enter a new era of predictive design for aerospace and other high-temperature applications.

Research at PNNL and the University of Texas at El Paso are addressing computational challenges of thinking beyond the list and developing bioagent-agnostic signatures to assess threats.

A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.

A new AI model developed at PNNL can identify patterns in electron microscope images of materials without requiring human intervention, allowing for more accurate and consistent materials science.