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.

Research at PNNL and the University of Texas at El Paso are addressing computational challenges of thinking beyond the list and developing bioagent-agnostic signatures to assess threats.

New apps help researchers with statistical analyses of data.

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

A team of researchers from PNNL provided technical knowledge and support to test a suite of techniques that detect genetically modified bacteria, viruses, and cells.

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

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.

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.

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