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

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.

Researchers at PNNL studied the effects of high temperatures and ionizing radiation on tungsten heavy alloys.

A new study led by PNNL researcher TC Chakraborty provides some answers about how irrigated agricultural land near cities affects urban heat stress.

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.

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

New datasets delineating global urban land support scientific research, application, and policy, but they can produce different results when applied to the same problem making it difficult for researchers to decide which to use.

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

Knowing future snow levels is key for managing the recovery of endangered bighorn sheep. PNNL scientists help to forecast how the sheep could fare.

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

A new study reveals just how costly Earth's tipping points can be.

The demand for energy is growing—and so is the technology supporting it. However, future development of power generation technologies could be affected by a key factor: material supply.

PNNL researchers co-organized a workshop on fusion commercialization with partners from industry and academia

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

Mengqi Zhao and Chris Vernon were selected to serve in editorial roles at JOSS for Earth science and data science, respectively.

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

PNNL researchers will pursue both fundamental and applied projects with this support from the Department of Energy

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.