Marcel Baer is a computational scientist working in PNNL’s Physical Sciences Division with a prominent effort in materials science and physical bioscience.

In the face of the COVID-19 pandemic, PNNL pivoted to online learning approaches to conduct critical national security training across the globe.

PNNL researchers are using explainable artificial intelligence to develop new ways to understand and interpret data for national security.

With quantum chemistry, researchers led by PNNL computational scientist Simone Raugei are discovering how enzymes such as nitrogenase serve as natural catalysts that efficiently break apart molecular bonds to control energy and matter.

Explainable artificial intelligence—understanding and explaining the reasoning behind AI decisions—is a necessity for national security.

Explainable AI helps scientists understand and untangle the threads of information that the system uses to make choices or recommendations.

In 2020, virtual Washington State University teams successfully worked together in a program sponsored by the National Nuclear Security Administration’s (NNSA) Office of International Nuclear Safeguards.

A recent edition of the Infrastructure Resilience Research Group Journal featured an article written by PNNL researchers Rob Siefken and Jake Burns about “Design Basis Threat and the Low Threat Environment.”

Connected moments math shortcut shaves time and cost of quantum calculations while maintaining accuracy.

As a physicist at PNNL, Jon Burnett’s work is about developing instruments to detect ultra-trace radionuclide signatures, analyze samples from around the world to look for evidence of nuclear explosions, and then interpret that information.

A new review paper led by senior research scientist Chun-Long Chen and featured on the cover of Accounts of Chemical Research summarizes advances by PNNL scientists in developing sequence-defined peptoids.

The article, “Nanoscale observations of Fe(II)-induced ferrihydrite transformation,” was selected for articles published in 2020.

PNNL researchers are examining the volatile radioisotope iodine as it evolves in the atmosphere and as it lands on a filtering surface.

The project received an Innovative and Novel Computational Impact on Theory and Experiment (INCITE) award, a highly competitive U.S. Department of Energy Office of Science program.

In a new video series, PNNL is highlighting six scientific and technical experts in the national security domain throughout the fall. Each was promoted to scientist and engineer Level 5, one of PNNL’s most senior research roles.

Researchers at PNNL have developed a bacteria testing system called OmniScreen that combines biological and synthetic chemistry with machine learning to hunt down pathogens before they strike.

A zoonotic coronavirus known as SADS-CoV that has killed many pigs has the ability to infect human cells, surprising scientists.

In a new video series this fall, PNNL is highlighting six scientific and technical experts in the national security domain. Each was promoted to Scientist and Engineer Level 5, one of PNNL’s most senior research roles.

Pacific Northwest National Laboratory researchers used machine learning to explore the largest water clusters database, identifying—with the most accurate neural network—important information about this life-essential molecule.

PNNL researchers and professional staff led discussions ranging from biothreats and climate change to science careers at the 2020 annual meeting of the American Association for the Advancement of Science, held this year in Seattle.