PNNL researchers created a model that helps materials scientists not only predict what is, but what could be.

The Co-Optimization of Fuels & Engines initiative recently outlined six years of research in a findings and impact report.

Scientists from PNNL and the U.S. Department of Agriculture-Forest Services’ Pacific Northwest Research Station have partnered to evaluate potential climate and wildfire adaptation scenarios and resulting benefits from restoration forestry.

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.

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

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.

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.