Ionic liquids bound to thin layers of graphene oxide produces more selective and efficient multi-layer membranes for water filtration.

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

Surface characterization allows researchers to explore the compatibility between different solvent-membrane combinations.

Researchers achieved the complete assignment of the infrared spectrum of a hydronium-water “magic number” cluster reported in Science in 2004.

A multi-institutional team has developed a deep learning framework to identify novel molecules as drug candidates.

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

Scientists created a benchmark dataset and explored new algorithms to better predict changes in the intensity of North Atlantic tropical cyclones.

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.

Researchers used deep learning methods to estimate the subsurface permeability of a watershed from stream discharge measurements.

Rigorous mathematical methods can help avoid unnecessary simplifications in numerical models of atmospheric clouds.

More accurate methods provide better results in numerical simulations of atmospheric clouds by avoiding accidental contradictions.

Researchers developed a new tool that combines artificial intelligence and microscopy data to rapidly test new materials for fusion reactor use.

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.