Increasing concentrations of ions slows the rate of particle aggregation in boehmite suspensions.

The ARPA-E Energy Innovation Summit brings together researchers, industry leaders, entrepreneurs, and investors to showcase the latest technologies shaping tomorrow’s energy landscape. This year, eight projects led by PNNL were featured.

PNNL chemical engineer Mariefel Olarte shares passion for mentoring students and the next-generation workforce.

A team from PNNL contributed several articles to the Domestic Preparedness Journal showcasing recent efforts to explore the emergency management and artificial intelligence research and development landscape.

PNNL Program Development Office Director Karl Mueller explains how the TEC4 project seeks to make advanced chemical computations accessible to all.

CACTUS provides AI-powered cheminformatics for autonomous science.

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

The Testing Risks of AI for National Security Taskforce will address critical safety and security concerns related to artificial intelligence.

New IDREAM research explores the effects of electrolyte species on boehmite nanoparticle aggregation in legacy wastes.

Computing and artificial intelligence aid scientists who seek to understand function-driven design and control of biological systems.

Klickitat Valley Health's fuel cell and microgrid project boosts rural healthcare, enhancing energy resilience and sustainability for social equity.

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

PNNL researchers work with industry partners to transform large language models.

University of Washington Professor David Baker won the 2024 Nobel Prize in Chemistry for computational protein design.

Pacific Northwest National Laboratory scientists discussed their materials research at Accelerate’24.

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

Three PNNL-supported projects are at the forefront of developing advanced data analytics technologies to enhance the U.S. power grid’s reliability, resilience, and affordability.

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

Hydrogen atoms released and trapped in irradiated gibbsite are destabilized by synthetic-precursor residuals.

Combining cold spray deposition and friction stir processing for a more efficient route to oxide dispersion strengthened steel.