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

PNNL is supporting the Department of Homeland Security Science and Technology Directorate's Chemical Security Analysis Center in improving capabilities to enhance detection and analysis of chemical threats.

The physics-informed machine learning optimization framework is now PNNL’s most popular open-source repository on GitHub.

PNNL researchers use chemometric analysis to model chemistry in molten salt.

Machine learning methods and data science can uncover hidden patterns in nanoparticle synthesis conditions and property outcomes.

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.

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

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.

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

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.