PNNL shared recent developments in artificial intelligence at the inaugural Special Competitive Studies Project AI for National Competitiveness Expo.

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.

PNNL researchers learn to control peptoid “handedness,” one key to precision drug delivery and diagnostics.

Reaching net-zero carbon emissions goals requires finding transformative paths to manage carbon in difficult-to-electrify economic sectors.

PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.

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

New research informs the development of foundational models for computational chemistry through hardware-software co-design.

A new look at a carbon capture solvent shows clusters and new types of carbon dioxide chemistry that could double carbon conversion.

As new paradigms in advanced computing take shape, computational chemistry researchers are finding new ways to solve challenging chemistry problems.

The Deep Vadose Zone program leads a treatability study for subsurface remediation of the Hanford Site’s Central Plateau.

PNNL is supporting the Department of Homeland Security Science and Technology Directorate's Chemical Security Analysis Center in improving capabilities to enhance detection and analysis of chemical threats.

PNNL researchers use chemometric analysis to model chemistry in molten salt.

A simple gel-based system separates metals ions from a model solution of dissolved battery electrodes without the need for specialty chemicals, membranes, or toxic solvents.

From energy storage to cybersecurity, this 2023 wrap-up offers a glimpse of PNNL innovations with a seasonal twist.

PNNL research unlocks key findings needed for potential commercial deployment of carbon mineralization technology.

PNNL researchers comment on the “Adversarial Machine Learning” white paper from the National Institute of Standards and Technology.

Elias Nakouzi brings 3D atomic force microcopy expertise to a project focused on understanding how hybrid bio-based nanomaterials assemble.

PNNL is one of the founding partners of the Trillion Parameter Consortium, which was formed to create reliable generative AI models.

A team of scientists at PNNL developed new computational models to predict the behavior of these impurities and reduce the expense and risk related to actinide metal production.

Resolving how nanoparticles come together is important for industry and environmental remediation. New work predicts nanoparticle aggregation behavior across a wide range of scales for the first time.