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

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.

A project team at PNNL has been honored with the Project Management Achievement Award for 2022 from the Department of Energy.

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.

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.

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

Bobbie-Jo Webb-Robertson is a leader with a PhD in decision sciences & engineering systems from Rensselaer Polytechnic Institute and experience in managing complex scientific programs and line organizations. She assumed the role 3/13/23.

How a model treats gas transfer behavior onto dust and sea salt particles significantly influences nitrate aerosols and their effects on climate.

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.

Among the highest forms of internal recognition, DOE Secretary Honor Awards recognize those who go above and beyond in service to the country.

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.

Data-driven autonomous technology to rapidly design and deliver antiviral interventions targeting SARS-CoV-2 to reduce drug discovery timeline and advance bio preparedness capabilities.

The Building and Environment journal named Drgoňa’s co-authored work one of 2022’s best papers.