Imaging ice microstructures formed by water freezing with atomic resolution for the first time.

Tailored assemblies mimic natural enzyme activity with significantly higher stability.

In high-entropy oxides, chromium tends to segregate during growth due to oxidation-induced migration of smaller, high-valence cations.

Researchers developed a way to precisely control dopant structures and locations in carbon-based materials.

PNNL's E-COMP initiative is helping unleash American energy innovation with advanced theories, models, and software tools to better operate power systems that rely heavily on high-speed power electronic control.

Researchers developed a pathway for the solution synthesis of core–crown heterostructures with controlled electronic properties.

Neural networks techniques enable the efficient capture of electron correlation effects in reduced-dimensionality spaces.

Under an optical field, nanoparticle attachment in a model system heavily depends on surface roughness.

In the search for rare physics events, extremely pure materials are essential. A partnership between PNNL and Ultramet has led to tungsten with low contamination from other elements.

Developing a new scheme to more efficiently solve quantum chemistry problems.

The Generator Scorecard, developed by PNNL in partnership with BPA, automates generator evaluations, reducing engineering workloads and improving grid reliability.

Chromium cations better adapt to changes in oxygen stoichiometry than iron cations.

Using interfacial charge transfer to control cation charges in oxide superlattices offers insights for designing functional heterostructures.

Pacific Northwest National Laboratory researchers developed a new theoretical method for studying highly correlated systems.

Calcium carbonate mineralization occurs through a multi-step process that begins with a dense liquid phase.

Nineteen PNNL-affiliated researchers have been named to Clarivate’s 2024 list of the world's most highly cited researchers.

A newly funded project will develop the fundamental understanding of phenomena that underpin material properties important for quantum applications.

In a recent publication in Nature Communications, a team of researchers presents a mathematical theory to address the challenge of barren plateaus in quantum machine learning.

Fully identifying what controls nanocrystal assembly requires incorporating non-traditional forces into models.

A new approach validates quantum algorithms for low-energy nuclear physics applications