Directionally biased electrostatic interactions are the key to significantly tuning the reduction potential of iron/sulfur clusters.

Researchers at PNNL advised elementary and middle school student teams with their problem-solving research for the FIRST® LEGO® League robotics competitions.

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

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

Electronic structure codes in a cloud environment can enable efficient computational chemistry simulations.

Molecular simulations highlight the key interactions between mineral surfaces and organic matter that lead to carbon stabilization in soils.

CACTUS provides AI-powered cheminformatics for autonomous science.

PNNL's accomplishments in FY 2024 help address the remaining challenges the wind industry faces.

PNNL will analyze current and projected transportation fuel dynamics, supply chain risks, and risk comparators with relevant sectors, such as transportation electrification.

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

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

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

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

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

Secretary Granholm praises heat pump workforce development efforts at this year's instructor training program.

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