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

Quantum simulators help navigate noise on GPU-based HPC systems.

Soil pores are reorganized when newly thawed permafrost soils experience freeze-thaw cycling.

PNNL researchers utilized the Peeters-Devreese-Soldatov formulation to improve the accuracy of calculations for quantum chemistry.

Ocean biogeochemical modeling software now available as open source to help researchers predict impacts of pollution, sea level rise, and climate change.

PNNL researchers developed a way to produce accurate simulations of complex physical systems using renormalized approximations across multiple scales

Hypergraphs can more faithfully identify important genes in complex immune responses.

The web-based tool, called PLATIPUS, is publicly available and interacts with publications available in the CovidScholar database.

Seeking a deeper understanding of when, where, and why large wildfires occur using explainable artificial intelligence.

Principles derived from coastal wetlands to describe wetland channel cross-sections were applicable to the Columbia River estuary, but not the tidal river.

New mathematical tools developed at PNNL hold promise to transform the way we operate and defend complex cyber-physical systems, such as the power grid.

Predictive models indicate cells regulate enzyme activity to maintain liquid center.

Contributions from researchers across Pacific Northwest National Laboratory (PNNL) were recently recognized in the preliminary findings of a Secretary of Energy Advisory Board (SEAB) report.

Pacific Northwest marsh carbon stocks are twice the global average. Long-term sea-level rise impacts will likely decrease ecosystem carbon stocks.

Scientists are exploring ways to derive more accurate predictions that can help with preparations for changing sea levels