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

Predicting how organisms’ characteristics respond to not only their genes, but also their environments (a nascent field called predictive phenomics), is extraordinarily challenging. Researchers at PNNL are using AI to tackle that challenge.

A new approach combining computational processes enables the more accurate simulation of nuclear quantum effects in molecular systems.

Quantum computing promises leaps in chemistry and materials science, and PNNL is preparing to realize its potential with a collaborative approach.

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 presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations.

PNNL chemist and AI scientist Nancy Washton invited to present on AI literacy at National Science Bowl.

Formal mathematics both reveals what’s inside the black box of AI algorithms and pushes the boundary of what’s possible within math as a discipline.

Over the next four years, PNNL and University of Arizona will develop open-source computational tools to better identify and characterize the viruses associated with the human microbiome.

Nine national laboratories, including PNNL, bring together over 1,000 scientists for a day to use AI to accelerate scientific discovery.

Scientists are using artificial intelligence and powerful computing to sculpt new molecules in an effort to treat disease.

Computing and artificial intelligence aid scientists who seek to understand function-driven design and control of biological systems.

The search for dark matter includes expertise in radio frequency signal detection, quantum sensing, and high-energy physics at PNNL.

Calculating quantum chemistry quickly allows scientists to refine their understanding of complex chemical systems.

Artificial intelligence, high-throughput experimentation, and materials science expertise combine to efficiently develop next-generation energy storage materials.

Researchers are planning for an electric grid that deploys machine learning to think ahead, plan for the worst, anticipate demand, and meet consumer needs safely and securely.

James Koch, a data scientist, developed a technique that enables high-fidelity remote sensing with limited data.

A team of researchers from Pacific Northwest National Laboratory and the Environmental Molecular Sciences Laboratory developed a new and flexible software tool called “Advanced Spectra PCA Toolbox.”

As new paradigms in advanced computing take shape, computational chemistry researchers are finding new ways to solve challenging chemistry problems.

A new AI model developed at PNNL can identify patterns in electron microscope images of materials without requiring human intervention, allowing for more accurate and consistent materials science.