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

Christina Lomasney moderates panel on commercialization pathways for emerging artificial intelligence and high-performance computing technologies.

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.

The Center for Continuum Computing at PNNL aims to integrate cloud platforms, high-performance computing, and edge devices into a seamless ecosystem that accelerates scientific discovery.

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

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

PNNL’s Center for Cloud Computing is addressing challenges in data management and hybrid cloud solutions with industry and academic partnerships.

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

A new quantum algorithm speeds up simulations of coupled oscillator dynamics.

In a new approach, quantum flow algorithms are used to simulate many-body systems on quantum computers and effectively describe chemical processes.