PNNL researchers present challenges and opportunities for fusing graph neural networks with deep reinforcement learning.

Joint appointments at PNNL and UTEP are teaming together to strengthen and protect sensitive data.

Computational chemistry libraries from NWChem, NWChemEX, SPEC, and more are now available on Azure Quantum Elements.

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

PNNL and The University of Texas at El Paso expand their premier partnership to include the Cyber Halo Innovation Research Program.

ExaGO, a power grid simulation and optimization platform developed at PNNL is the first of its kind to run on Frontier.

The popular approach of organizing soil bacteria into fast- or slow-growing groups is problematic because most bacteria grow at comparable rates in soil.

Metabolomics analysis revealed differences in metabolome profiles for two distinct modes of fungal wood decay.

Research from PNNL and the University of Washington demonstrates the extension of the MBE for periodic systems and its use to decompose the lattice energies of different ice polymorphs.

Methods review suggests ways to use artificial intelligence and machine learning to handle missing data when integrating two or more omics approaches.

Under Secretary for Science and Innovation at the Department of Energy Geri Richmond visited PNNL.

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

Variations in burn severity are a key control on the chemical constituents of dissolved organic matter delivered to streams within a single burn perimeter.

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

Department of Energy, Office of Science Director Asmeret Asefaw Berhe visited PNNL to learn about the Lab’s drive to conduct discovery science, commitment to science for an equitable future, and development of a diversified STEM workforce.

A multi-omics analysis provides the framework for gaining insights into the structure and function of microbial communities across multiple habitats on a planetary scale

A rich and largely untapped reservoir of lipids in soil environments was used to examine microorganisms’ physiological responses to drying-rewetting cycles.

Snekmer is an application for building and searching protein family models and novel sequence clusters.

Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.

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