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

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.

Directionally biased electrostatic interactions are the key to significantly tuning the reduction potential of iron/sulfur clusters.

Erik Lentz and Christian Boutan will research quantum information science-enabled discoveries in high energy physics with these awards.

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

A PNNL team has developed an energy- and chemical-efficient method of separating valuable critical minerals from dissolved solutions of rare earth element magnets.

Surface manganese mining from nodules in South Dakota is now possible with a new scalable selective precipitation process.

Researchers have created an algorithm that slashes the calculations necessary to prepare and customize data for quantum computing.

A potential quantum advantage for fluid dynamics simulations from molecular to atmospheric scales with a new formula for success.

Cobalt substitution for magnesium in a model mafic mineral dramatically slows reaction with carbon dioxide to form a cobalt-doped carbonate.

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

Precipitation with sodium hydroxide in a flow-gel device enables the rapid extraction of magnesium from seawater in a new scalable process.

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

The search for dark matter includes expertise in radio frequency signal detection, quantum sensing, and high-energy physics at PNNL.

ARQUIN computational pipeline provides a holistic framework for simulating a system with distributed quantum computing ingredients.

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.

Three PNNL researchers will receive five continuous years of funding for projects through highly competitive DOE Early Career Research Program awards.

Bylaska and team will aim to make computational design of functional materials more targeted, impactful, and user friendly through this award.

From enhancing biofuel crops to setting up automated experiments and improving energy storage—interns at PNNL are making an impact.

Early life exposure to polycyclic aromatic hydrocarbons (PAHs), found in smoke, has been linked to developmental problems. To study the impacts of these pollutants, PAH metabolism in infants and adults were compared.