In the search for rare physics events, extremely pure materials are essential. A partnership between PNNL and Ultramet has led to tungsten with low contamination from other elements.

Neural networks techniques enable the efficient capture of electron correlation effects in reduced-dimensionality spaces.

Erik Lentz and Christian Boutan will research quantum information science-enabled discoveries in high energy physics with these awards.

Developing a new scheme to more efficiently solve quantum chemistry problems.

Researchers have created an algorithm that slashes the calculations necessary to prepare and customize data for quantum computing.

Researchers at PNNL are pursuing new approaches to understand, predict and control the phenome—the collection of biological traits within an organism shaped by its genes and interactions with the environment.

A potential quantum advantage for fluid dynamics simulations from molecular to atmospheric scales with a new formula for success.

Led by interns from multiple DOE programs, a newly expanded dataset allows researchers to use easy-to-obtain measurements to determine the elemental composition of a promising carbon storage mineral.

This project sought to assure that research activities centered around different sampling and monitoring efforts in northwest Ohio would not disturb any historical cultural resources.

A multi-institutional team of researchers systematically compared extraction techniques for characterizing plant litter composition that relies on organic matter extraction.

Pacific Northwest National Laboratory researchers developed a new theoretical method for studying highly correlated systems.

Nineteen PNNL-affiliated researchers have been named to Clarivate’s 2024 list of the world's most highly cited researchers.

A new database allows researchers to connect lattice parameters and composition in olivine.

New methodology can evaluate the potential for carbon storage and critical mineral recovery in undercharacterized geological settings.

The search for dark matter includes expertise in radio frequency signal detection, quantum sensing, and high-energy physics at PNNL.

Research identifies the mechanisms through which peptoids affect ions in solution and a mineral surface, increasing the rate of carbonate crystal growth.

ARQUIN computational pipeline provides a holistic framework for simulating a system with distributed quantum computing ingredients.

In a recent publication in Nature Communications, a team of researchers presents a mathematical theory to address the challenge of barren plateaus in quantum machine learning.

Three PNNL researchers will receive five continuous years of funding for projects through highly competitive DOE Early Career Research Program awards.

Bylaska and team will aim to make computational design of functional materials more targeted, impactful, and user friendly through this award.