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

Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.

Study shows the advantages of top-town proteomics for characterizing histones.

Integrating new functions into a genome-scale model enabled researchers to simulate and experimentally validate viral hijacking pathways.

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

A microbial community expresses a phenotype of interest ~20 percent higher than the sum of each member species.

Structural proteomics reveal how cyanobacteria adapt to changing light.

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.

Now, anyone can easily explore and access data from a nationwide map of data centers, the infrastructure that powers them, and projections of future data center locations.

Researchers use multiomics to confirm role of lipid synthesis during infection, leading to model of how to restrict virus replication.

PNNL researchers Daniel Deng and Yehia Ibrahim join the 2025 class of National Academy of Inventors Fellows.

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

Capturing protein structural changes in host cells exposes vulnerabilities exploited by viruses to hijack cellular machinery.

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

PNNL researchers developed a new computational workflow to study day-to-night gene expression dynamics of cyanobacterium in real time.

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

Understanding molecular modifications of proteins in the red yeast could help scientists re-engineer the organism’s phenotype.

Researchers from PNNL and Parallel Works, Inc., applied machine learning methods to predict how much oxygen and nutrients are used by microorganisms in river sediments.

This study used historical data, remote sensing, and aquatic sensors to measure how far wildfire impacts propagated through the watershed after the 2022 Hermit’s Peak/Calf Canyon fire, New Mexico’s largest wildfire in history.