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

PNNL recently joined the Department of Homeland Security for two technical meetings exploring national security research spanning the threat realm, from chemical and biological attacks to adversarial artificial intelligence.

ACMD staff contributed to 30 years of atmospheric data collection for the Department of Energy’s Atmospheric Radiation Measurement user facility.

Joint appointments at PNNL and UTEP are teaming together to strengthen and protect sensitive data.

Purvine shares how research-collaboration workshops focused for women in data science create a sense of inclusion, empowerment, and networking.

By adding rain, snow, and rain-on-snow precipitation data to a background model, a new scheme pinpoints local flood risks in order to improve the design of small-scale hydrological infrastructure.

Unveiling diverse scenarios and implications for water resource management in a rapidly changing world.

The papers were featured in the security and quantum sessions at the 2023 International Symposium on Computer Architecture.

Lauren Charles, a chief data scientist at PNNL, joined the editorial board of the largest open access journal published by Nature Research.

PNNL researchers are helping to better define the need for grid energy storage in future clean energy scenarios, as well as working to improve technologies for storing renewable energy so it's available when and where it's needed.

Using a combination of satellite data and modeling to study the temperatures and humidity people might feel in urban areas, researchers have pinpointed who in the U.S. is most vulnerable to heat stress.

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

Quantifying the long-term destination of anthropogenic emissions and robustness of global carbon sinks.

New study identifies crucial factors for more accurate hydrological predictions in the Community Land Model version 5.

Groundwater modeling of irrigation return flow dynamics is important for improving estimates of water availability.

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

Adam Attarian led a new panel session, titled National Labs and the GEOINT Mission, at the GEOINT 2023 Symposium, held May 21–24, 2023.

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

ExaGO, a power grid simulation and optimization platform developed at PNNL is the first of its kind to run on Frontier.

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.