The low-temperature breakdown of polyolefins is driven by carbenium ions generated from chloroaluminate ionic liquids.

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

Tuning the environment of supported palladium atoms with hydrogen treatment enhances their catalytic activity.

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

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

A switchable single-atom catalyst is activated in the presence of surface intermediates and reverts to its stable inactive form when the reaction is completed.

Increasing the concentration of ions in zeolite micropores leads to a significant increase in the rate of acid catalyzed reactions.

Electronic modifications from hydrogen spillover onto the oxide support increase the activity of single-atom rhodium catalysts.

ChemReasoner combines decades of chemistry knowledge with large language models to propose strategies for effective new catalysts.

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.

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.

Leaders from the DOE Office of Energy Efficiency and Renewable Energy visited PNNL October 19–20 for a firsthand look at capabilities and research progress.

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.

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

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

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

Methods review suggests ways to use artificial intelligence and machine learning to handle missing data when integrating two or more omics approaches.