PNNL’s Center for Cloud Computing is addressing challenges in data management and hybrid cloud solutions with industry and academic partnerships.

PNNL's new energy storage facility now houses the Lab's vehicle battery research programs, which have more lab space and a slew of new capabilities.

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

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.

Models have identified a new source of anthropogenic emissions driving the observed weakening of the Atlantic Meridional Overturning Circulation.

Spatial proteomics enables researchers to link protein measurements to features in the image of a tissue sample, which are lost using standard approaches.

PNNL researchers outline open problems in different scientific areas in the Journal of Combinatorics.

Led by PNNL, the new Cathode-Electrolyte Interphase Consortium has been launched, involving 10 national laboratories and 10 universities.

A PNNL-developed computational framework accurately predicts the thermomechanical history and microstructure evolution of materials designed using solid phase processing, allowing scientists to custom design metals with desired properties.

Research published in Journal of Manufacturing Processes demonstrates innovative single-step method to manufacture oxide dispersion strengthened copper materials from powder.

Multidisciplinary teams from research and industry are brought together using management systems developed by a team of software engineers at PNNL.

HiSVSIM enables large-scale quantum simulations through graph partitioning.

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