PNNL researchers are studying catalysts that are key to dealing with waste and producing sustainable energy.

Creating films with atomic precision allows researchers moving to the Energy Sciences Center to identify small, but important changes in the materials.

PNNL has developed seaweed-based inks and materials for 2-D and 3-D printing that can be used for a multitude of applications in the art, medical, STEM, and other fields.

PNNL data scientists are refining their artificial intelligence tools with COVID-19 data to advance therapeutics and treatments for a future pandemic.

The U.S. Department of Energy has selected the Scalable Predictive Methods for Excitations and Correlated Phenomena project to receive funding to develop software for chemical research.

Research into practical systems for chemical energy storage will be a focus at the Energy Sciences Center.

Bojana Ginovska leads a physical biosciences research team headed for PNNL's new Energy Sciences Center. She uses the transformative power of molecular catalysis and enzymes to explore scientific principles.

New research aids the design of more efficient water purification technologies.

Marcel Baer is a computational scientist working in PNNL’s Physical Sciences Division with a prominent effort in materials science and physical bioscience.

A shoe scanner may allow people passing through security screening to keep their shoes on. PNNL built the scanner based on the same technology it used to develop airport scanners. It's licensed to Liberty Defense.

With quantum chemistry, researchers led by PNNL computational scientist Simone Raugei are discovering how enzymes such as nitrogenase serve as natural catalysts that efficiently break apart molecular bonds to control energy and matter.

Connected moments math shortcut shaves time and cost of quantum calculations while maintaining accuracy.

The project received an Innovative and Novel Computational Impact on Theory and Experiment (INCITE) award, a highly competitive U.S. Department of Energy Office of Science program.

Pacific Northwest National Laboratory researchers used machine learning to explore the largest water clusters database, identifying—with the most accurate neural network—important information about this life-essential molecule.