The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.

AAAS Fellows are a distinguished cadre of scientists, engineers, and innovators who have been recognized for their achievements across disciplines.

A paper by PNNL researchers has been chosen as one of 32 NIEHS Papers of the Year—a top 1% honor in the research field.

Microbes that were previously frozen in soils are becoming more active. This study demonstrates the diverse RNA viral communities found in thawed permafrost.

PNNL will partner with Commonwealth Fusion Systems to develop innovative steel materials for fusion applications under the INFUSE program

PNNL Biomedical Scientist Geremy Clair has taken on new roles as an editor for two journals; Frontiers In Cellular And Infection Microbiology and Frontiers In Molecular Biosciences.

Findings show method to better prepare human lung tissue for study; it represents the latest mass spectrometry-based omics advances.

PNNL’s contributions over the last year span scientific discovery, sustainable energy and national security.

Clarivate recently released the list of Highly Cited Researchers for 2022.

The Joint Appointment program at PNNL is one of the most diverse among other U.S. national laboratories, involving nearly 60 universities and research institutions in the United States and abroad.

Research published in Journal of Manufacturing Processes demonstrates innovative single-step method to manufacture oxide dispersion strengthened copper materials from powder.

To thwart pathogens, researchers in the epidemiology field of infectious disease (ID) prediction are continuously trying to forecast when, where, and how an ID event will occur.

The American Society of Mechanical Engineers recognized radioactive material transportation expert Matt Feldman with its 2022 Dedicated Service Award.

New research findings published in Science Advances (November 2022), help explain the progression of Alzheimer-related dementia in each patient. The findings outline a biological classification system that predicts disease severity.

Data-driven experiments can help determine best practices for Random Forest machine learning to predict water quality, particularly nitrate levels.

Studying how microorganisms behave in space could facilitate farming in the final frontier and lead to better crops on Earth.

Silicone wristbands could be more convenient option for collecting exposure data.

Organismal contribution and community interaction drive soil organic matter breakdown by microbes.

Direct visualization of metal atoms during shear deformation has broad applications from battery design to vehicle lightweighting.

PNNL researchers use machine learning and data analytics to assist with detection of nuclear proliferation and nuclear material trafficking.