Researchers from PNNL, CSET, and Google DeepMind delved into the potential risks of artificial intelligence in a roundtable discussion.

Partitioning measured ice nucleating particle concentrations into individual particle types leads to a better understanding of the sources and model representations of these particles.

Pasco School District teachers join PNNL for an immersive teaching experience.

Researchers from Pacific Northwest National Laboratory created and embedded a physics-informed deep neural network that can learn as it processes data.

The Human Factors Symposium took place at Discovery Hall at PNNL in May 2023. Fifty-seven attendees participated in the three-day event representing 15 different institutions.

Different sources of low-level moisture perturbation can result in differences in the timing and location of a large, modeled storm.

Randomly constructed neural networks can learn how to represent light interacting with atmospheric aerosols accurately at a low computational cost and improve climate modeling capabilities.

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

PNNL welcomes six Department of Energy Office of Science Graduate Student Research awardees from across the nation.

Computational chemistry libraries from NWChem, NWChemEX, SPEC, and more are now available on Azure Quantum Elements.

The complex interactions between the land and atmosphere can affect cloud growth.

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

PNNL and The University of Texas at El Paso expand their premier partnership to include the Cyber Halo Innovation Research Program.

Assessing observed weather conditions that support or suppress the growth of clouds into deep precipitating storms during the Cloud, Aerosol, and Complex Terrain Interactions experiment.

Multiple concurrent single-particle measurements help develop a quantitative and predictive understanding of secondary organic aerosols.

Applying a novel framework to a climate model reveals new characteristics about how elevated concentrations of aerosols affect clouds.

A deep learning model overcomes persistent challenges in emulating long-term simulations of secondary organic aerosols.

Under Secretary for Science and Innovation at the Department of Energy Geri Richmond visited PNNL.

Advances in understanding the nature of the chemical bond are featured in this special issue of The Journal of Chemical Physics.

Performing closure studies using aerosol size, aerosol composition, and cloud condensation nuclei measurements of mixed aerosol from the Southern Great Plains region.