Catalysts that efficiently transfer hydrogen for storage in organic hydrogen carriers are key for more sustainable generation and use of hydrogen. New research identifies activity descriptors that can accelerate novel catalyst development.

The 98th ACS Colloid and Surface Science Symposium will be co-organized by Shuai Zhang and Jim De Yoreo.

Visual Sample Plan, a free software tool developed at PNNL that boosts statistics-based planning, has been recognized with a 2024 Federal Laboratory Consortium Award.

PNNL is one of the founding partners of the Trillion Parameter Consortium, which was formed to create reliable generative AI models.

Researchers created a methodology to design custom network topologies that improve the execution time for scientific applications.

1-propanol converts from a monomer into a trimer at aluminum sites within zeolite pores.

Tumeo will lead the ChemComp project, which was awarded for Exploratory Research in Extreme Scale Science

Robert Rallo from Pacific Northwest National Laboratory will direct a machine learning thrust for a new Department of Energy-funded project led by SLAC National Accelerator Laboratory.

Stinis will serve as a principal investigator on two of the four projects selected for funding by the Advanced Scientific Computing Research program.

Researchers unveil user-friendly software to keep complex projects on budget and on schedule.

Studying single crystal reactions provides molecular-level insight into catalytic processes.

Wendy Shaw is a new Fellow of the Royal Society of Chemistry, honored for her leadership role and impacts on the chemical sciences.

Six PNNL projects were selected as part of the Department of Energy’s Funding for Accelerated, Inclusive Research.

The Rapid Assessment of Platform Technologies to Expedite Response project aims to prepare against future pandemics.

The award was given at the 29th International Conference on Information, Communication and Automation Technologies.

Zhijie (Jay) Xu will apply his expertise in multiscale modeling to this editorial role.

PNNL researchers present challenges and opportunities for fusing graph neural networks with deep reinforcement learning.

ACMD staff contributed to 30 years of atmospheric data collection for the Department of Energy’s Atmospheric Radiation Measurement user facility.

Research in the Energy Sciences Center explores how heat changes in chemical reactions, paving the way for more efficient fuels and processes.

Highly precise and controllable single-atom catalysts are affected by reaction conditions, which can alter the bonding around the atoms and the activity.