Separating quantum circuit wiring from molecular data allows researchers to more efficiently perform quantum simulations

Yong Wang’s joint appointment connects PNNL, WSU, and the next generation of scientists.

Demonstrated that cathodic potential accelerates aqueous phase benzaldehyde hydrogenation.

Global exercise advances nuclear forensic science and investigative capabilities.

Amine functionalized graphene oxide yields laminates with an enhanced capacity to adsorb rare earth elements, improved water transport, and greater stability for effective membrane-based separation of critical minerals.

Establishing how electronic redistribution, protonation, and solvent rearrangement are coupled during a proton-coupled electron transfer process.

Using multiple specialized techniques, IDREAM researchers gained a better understanding of how trace impurities within gibbsite affect the hydrogen yield.

By selectively stabilizing either a right- or left-handed helical pose of its substrate, an enzyme tunes the final product.

Research provided definitive spectroscopic characterization, reactivity, and deactivation of a reactive monomeric diphosphine-copper hydride intermediate.

PNNL scientists were among the specialists keeping the crowds safe at the recent FIFA World Cup competition.

Five PNNL innovations earned 2026 R&D 100 Awards for advances in biotechnology, manufacturing, critical minerals, and nuclear security.

An innovative new approach enables direct measurements of electronic effects at solid–liquid interfaces.

New software enables the study of nuclear quantum effects in large, condensed phase systems up to 10× faster than traditional methods.

Protein and water environments set the overall redox scale of iron-sulfur clusters, but local coordination geometry fine-tunes the final potential.

New theoretical framings of quantum dynamics enable better quantum-based simulation of molecular systems and dynamics.

Chiral nanotubes facilitate the direction-specific diffusion of carbon dioxide to be faster than nitrogen gas.

Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.

At the summit, PNNL engaged with partners and investors, highlighting seven projects that accelerate the deployment of new energy technologies.

A suite of new modeling methods gives researchers better tools to predict molecular behavior inside cavities.

PNNL has developed a next-generation electrical resistivity tomography system for DOE that uses E4D software and AI-enhanced modeling to produce real-time subsurface images that help guide environmental remediation decisions.