PNNL researchers outline open problems in different scientific areas in the Journal of Combinatorics.

Advances in understanding the nature of the chemical bond are featured in this special issue of The Journal of Chemical Physics.

Deep learning helps make predictions on the effects of the El Niño-Southern Oscillation on river flows more accurate.

PNNL welcomes new joint appointments to expand the research productivity and scientific impact of both PNNL and the university partners, broadening the base of expertise at each institution and helping to build interdisciplinary teams.

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.

Advancing the science of radiation, especially among students at minority-serving institutions, is the goal of one of the Department of Energy’s newest consortia.

HiSVSIM enables large-scale quantum simulations through graph partitioning.

PNNL joint appointee Auroop Ganguly uses data science to study the impacts of climate change.

A new web-based tool provides easy-to-understand progress metrics and other data about groundwater cleanup sites overseen by the DOE Office of Environmental Management.

Launching a premier partnership, PNNL and the University of Texas El Paso (UTEP) signed a memorandum of agreement on UTEP’s campus on October 6.

SEA-CROGS is one of four Mathematical Multifaceted Integrated Capability Centers selected for support by the Department of Energy.

New research makes molecular-level calculations of water faster and easier.

PNNL researchers teamed up with SambaNova to enhance artificial intelligence for scientific computing.

New research from PNNL results in faster and more accurate variational quantum algorithm training.

PNNL scientists developed new capabilities to study materials under extreme conditions.

Researchers use machine learning to speed up the analysis of aerosol datasets from mass spectrometry.