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

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

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.

Combining X-ray-based techniques allows researchers to locate and characterize small concentrations of electrons in complex semiconductor films.

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

Researchers created a methodology to design custom network topologies that improve the execution time for scientific applications.

The pathway of oxygen ion movement can be controlled based on the initial structure and substrate of strontium iron oxide thin films.

Researchers unveil user-friendly software to keep complex projects on budget and on schedule.

PNNL researchers present challenges and opportunities for fusing graph neural networks with deep reinforcement learning.

PNNL researchers demonstrated a simple method to create stable, identical nanoparticles of PdTe2-like composition, which is known to be superconducting, on a WTe2 TMD support.

PNNL researchers outline open problems in different scientific areas in the Journal of Combinatorics.