Secretary Wright visited PNNL's campus in Richland, Washington, late last year to meet with innovators in chemistry, biology, computing, and more.

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.

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

AI for Good event commemorates 60 years of community engagement, features AI applications, and offers practical AI tools to empower people.

This summer, scientists at PNNL led discussions on their latest research related to artificial intelligence and One Health at the Health and Environmental Sciences Institute conference.

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

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

PNNL researchers have published their paper, “Introducing Molecular Hypernetworks for Discovery in Multidimensional Metabolomics Data,” in the Journal of Proteome Research.

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.

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

CACTUS provides AI-powered cheminformatics for autonomous science.

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

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

PNNL researchers work with industry partners to transform large language models.

University of Washington Professor David Baker won the 2024 Nobel Prize in Chemistry for computational protein design.

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.

ChemReasoner combines decades of chemistry knowledge with large language models to propose strategies for effective new catalysts.

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