Localized gradients in magnetic fields have long-range effects on the concentration of rare earth ions in solution, facilitating field-driven extraction of critical minerals.

Studying a series of rare earth element compounds revealed differences in their magnetic behavior that will be leveraged for separations.

Delivering excellence in power systems and electric grid advancement earns two PNNLers the association’s top distinction.

Researchers at PNNL share a research- and practitioner-informed approach to assess the threat landscape, elicit and integrate feedback into solutions, and ultimately share outcomes with the emergency response and public safety community.

An award-winning computing method called poly-streaming accelerates the analysis of massive datasets while using limited memory.

PNNL brings its expertise to the space domain by joining an organization that facilitates collaboration across the global space industry.

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

As Laboratory Director Steve Ashby pens his last monthly column, he highlights PNNL’s accomplishments with pride and gratitude.

Nanoscale domains of magnetically susceptible critical materials encounter enhanced magnetic interactions under external magnetic fields, providing a promising new avenue for separations.

David Heldebrant was selected for the 2025 Distinguished Service Award from the American Chemical Society Division of Energy & Fuels, recognizing his impact to energy and fuels chemistry.

Three research teams led by PNNL organizers achieve journal publications following the Mathematics Research Communities program.

PNNL showcased its expertise in AI, machine learning, and related domains at the International Conference on Machine Learning.

The size of graphene oxide sheets affects the ability of assembled membranes and adsorbents to separate rare earth elements.

Scientists have tapped AI and powerful computing to speed up how quickly officials are able to learn important details about nuclear events.

Expanding the nation’s largest wind power plant time series database; helping grid planners navigate a resilient energy future with better data.

Two new publications provide emergency response agencies with critical insights into commercially available unmanned ground vehicles used for hazardous materials response.

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

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 can now quantify uncertainty in trained AI models aimed at solving difficult chemistry, biology and materials science problems.