PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.

The SHASTA program is doing a deep dive on subsurface hydrogen storage in underground caverns, helping to lay the foundation for a robust hydrogen economy.

In a new approach, quantum flow algorithms are used to simulate many-body systems on quantum computers and effectively describe chemical processes.

Models have identified a new source of anthropogenic emissions driving the observed weakening of the Atlantic Meridional Overturning Circulation.

Identifying how curvature affects the doping and hydrogen binding energies of carbon-based materials provides a framework for designing hydrogen storage materials.

An initiative from Washington State University and Snohomish County leaders is aiming to make Paine Field a nexus for testing and improving sustainable aviation fuels made from non-petroleum materials.

PNNL researchers comment on the “Adversarial Machine Learning” white paper from the National Institute of Standards and Technology.

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.

PNNL’s DataLife tool analyzes and identifies bottlenecks in scientific workflows.

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

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

Scientists at PNNL were awarded nearly $12 million to better understand pathogens, how they spread, and how to prepare the nation against future outbreaks.

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.

Summer internship programs with partnering colleges provide pathways for students to explore careers in science, technology, engineering, and math.

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

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

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

Research from PNNL and the University of Washington demonstrates the extension of the MBE for periodic systems and its use to decompose the lattice energies of different ice polymorphs.

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