PNNL scientists investigate the promising properties of a common, Earth-abundant salt.

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.

To overcome high-performance computing bottlenecks, a research team at PNNL proposed using graph theory, a mathematical field that explores relationships and connections between a number, or cluster, of points in a space.

Researchers have knitted together three large databases of information to help cybersecurity experts detect and prevent attacks.

Three PNNL authored papers were accepted as posters to the ICLR 2023 Workshop on Physics for Machine Learning and Workshop on Mathematical and Empirical Understanding of Foundation Models.

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

Water affects the intermediates present on the surface of a metal oxide catalyst during ketonization.

Research.com ranked PNNL joint appointee George Karniadakis as one of the top mathematicians in the world.

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

Vrabie co-organized the 2022 Workshop on Artificial Intelligence for Isotope R&D and Production for the Department of Energy Isotope Program.

PNNL-developed catalytic process and catalyst to upgrade ethanol to sustainable aviation fuel wins American Chemical Society award.

PNNL’s Sameera Horawalavithana joins the journals IEEE Transactions on Artificial Intelligence and Humanities and Social Sciences Communications.

Deep learning helps make predictions on the effects of the El Niño-Southern Oscillation on river flows more accurate.

Xantheas was one of four professors in 2021/2022 to be selected by the Göttingen Academy of Sciences and Humanities for this award.

Artificial intelligence may help humans and computers work together to monitor materials that could be used to make weapons.

Newly developed models can help rapidly optimize systems to meet the need for rapidly deployable commercial carbon capture.

Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.

A new system can convert common plastics into valuable fuel molecules.