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

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

PNNL has developed a next-generation electrical resistivity tomography system for DOE that uses E4D software and AI-enhanced modeling to produce real-time subsurface images that help guide environmental remediation decisions.

RemPlex and IAEA convene international leaders to tackle recruitment and retention in complex environmental cleanup efforts.

CACTUS provides AI-powered cheminformatics for autonomous science.

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

Researchers found that in a future where the Great Plains are 4 to 6 degrees Celsius (°C) warmer as projected in a high-emission scenario, these storms could bring three times more intense rainfall.

PMedIC, the PNNL and OHSU joint research collaboration, contributes to student success and impact in biomedicine.

A compilation of soil viral genomes provides a comprehensive description of the soil virosphere, its potential to impact global biogeochemistry, and an open database for future investigations of soil viral ecology.

A recent study suggests a higher global mean precipitation increase and narrows the uncertainty range.

Data reveals mechanisms in our bodies that can help us fight viral pathogenesis.

New apps help researchers with statistical analyses of data.

Researchers devised a quantitative and predictive understanding of the cloud chemistry of biomass-burning organic gases helping increase the understanding of wildfires.

With the help of the U.S. State Department, PNNL climate scientists assisted counterparts in Malaysia and Thailand in exploring carbon neutral goals.

PNNL’s Center for the Remediation of Complex Sites convened attendees from around the world to discuss challenges associated with environmental contamination.

Spatial proteomics enables researchers to link protein measurements to features in the image of a tissue sample, which are lost using standard approaches.

The new PTM-Psi workflow opens the door for uncovering molecular insights within proteomics datasets.

A team of scientists at PNNL developed new computational models to predict the behavior of these impurities and reduce the expense and risk related to actinide metal production.

PNNL is one of the founding partners of the Trillion Parameter Consortium, which was formed to create reliable generative AI models.