A research team from Pacific Northwest National Laboratory used machine reasoning to schedule scientific workflows while guaranteeing quality of service (QoS).

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

A recent article highlighted a PNNL report focused on post-quantum cryptography and electric vehicle charging infrastructure.

Darío Gil, DOE's Undersecretary for Science, makes his inaugural visit to PNNL.

Nanosize platelets of an aluminum material slide and join in a staggered orientation to form larger crystals.

The Scientific Discovery through Advanced Computing institute will focus on making scientific machine learning accessible to domain scientists.

An approach combining multiple theories increases the accuracy of quantum-based simulations without increasing computational demands.

IDREAM researchers uncovered how dimeric complexes control how aluminum hydroxide minerals dissolve.

This achievement earned DIMPLES a Best Paper award at the ACM International Conference on Supercomputing 2025.

Trace amounts of iron and chromium can incorporate into gibbsite, affecting how the mineral dissolves.

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.

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

Increasing concentrations of ions slows the rate of particle aggregation in boehmite suspensions.

New IDREAM research explores the effects of electrolyte species on boehmite nanoparticle aggregation in legacy wastes.

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

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

Hydrogen atoms released and trapped in irradiated gibbsite are destabilized by synthetic-precursor residuals.

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.

When exposed to a sufficiently high dose of ionizing radiation, gibbsite and boehmite are less susceptible to dehydration-induced breakdown.