A new approach developed by PNNL scientists improves the accuracy of patient diagnosis up to 20 percent when compared to other embedding approaches.

Through her role in the Department of Energy’s Advanced Scientific Computing Research-supported ExaLearn project, Jenna Pope is developing deep learning approaches for finding optimal water cluster structures for a variety of applications.

Scientists at PNNL are bringing artificial intelligence into the quest to see whether computers can help humans sift through a sea of experimental data.

The U.S. Department of Energy’s Pacific Northwest National Laboratory (PNNL) is the recipient of two R&D 100 awards and one gold medal.

We are all citizens of watersheds, the terrestrial landscapes that determine how much water we have, how clean it is, where it goes, and how fast.

In today’s digital age, the rabbit hole of connected information can be not only a time sink, but downright overwhelming. Even for high-performance computers.

Twenty-four analysts from U.S. intelligence organizations met in August for a machine learning activity with PNNL researchers Nicole Nichols, Jeremiah Rounds, Lawrence Phillips, and Brian Kritzstein.

A new study examines how back how back-to-back extreme events can affect a forest landscape.

Three powerhouses in the realm of artificial intelligence have become partners in a new research center created by the U.S. Department of Energy.

Geochemistry postdoctoral research assistant Quin R.S. Miller has been elected to a three-year term on the Clay Minerals Society Council.

Biogeochemical activity in the hyporheic zone (HZ), sediments where the flowing waters of a river mix with shallow groundwater, supports many of the biological processes that occur within a watershed.

Turbulent vertical fluxes of heat, water vapor, and carbon dioxide (CO2) occur constantly between land surfaces and the atmosphere.

Co-authors of a paper in Hydrological Processes led by PNNL researchers Zhangshuan Hou, Timothy Scheibe, and Christopher Murray, produced a map that identifies different classes of sediments which compose the riverbed along the Hanford ...

A multi-institutional team of scientists developed a new sensitivity analysis framework using Bayesian Networks to quantify which parameters and processes in complex multi-physics models are least understood.

Reactive transport models (RTMs) are used to describe and predict the distribution of chemicals in time and space, in both marine and terrestrial (surface and near-surface) environments where microbially-mediated processes govern...

Hydrologic exchange flows (HEFs) increase the contact between river water and subsurface sediments thereby playing a critical role in biogeochemical and ecological functions along river corridors.

Hydrologic exchange fluxes (HEFs) between rivers and surrounding subsurface environments strongly influence water temperatures and biogeochemical processes. Yet, quantitative measures of their effects on the strength and direction of such e

Trouble on the electric grid might start with something relatively small: a downed power line, or a lightning strike at a substation. What happens next?

Pacific Northwest National Laboratory is leading efforts to address next-generation computing’s critical role in protecting the nation from cybersecurity threats.

The Soil Science Society of America presents Nik Qafoku with the 2019 Jackson Award for contributions in soil chemistry and mineralogy—ranging from agricultural fertilizer efficiency in Albania to soil contaminant transport at Hanford.