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

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.

PNNL energy storage experts express need for continued investment in developing and deploying long-duration energy storage.

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

PNNL welcomes six Department of Energy Office of Science Graduate Student Research awardees from across the nation.

Recovery Simulator and Analysis tool further strengthens PNNL’s short-term resilience planning efforts for Puerto Rico.

PNNL’s extensive portfolio of buildings-grid research included three projects that helped answer some of the technical questions related to leveraging energy consumption in buildings to enhance grid operations.

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.

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

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

The Northwest Connected Communities Summit brought together representatives of five Department of Energy-funded Connected Communities Projects to share ideas and discuss potential collaboration opportunities.

Department of Energy Office of Electricity Assistant Secretary Gene Rodrigues makes his first visit to PNNL.

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

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

Department of Energy, Office of Science Director Asmeret Asefaw Berhe visited PNNL to learn about the Lab’s drive to conduct discovery science, commitment to science for an equitable future, and development of a diversified STEM workforce.

A multi-omics analysis provides the framework for gaining insights into the structure and function of microbial communities across multiple habitats on a planetary scale

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.