PNNL will engage with transmission planners and other regional partners through technical assistance and listening sessions with the goal of exploring opportunities to integrate equity into transmission planning.

Researchers created a methodology to design custom network topologies that improve the execution time for scientific applications.

Researchers unveil user-friendly software to keep complex projects on budget and on schedule.

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

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

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

Methods review suggests ways to use artificial intelligence and machine learning to handle missing data when integrating two or more omics approaches.

The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.

To thwart pathogens, researchers in the epidemiology field of infectious disease (ID) prediction are continuously trying to forecast when, where, and how an ID event will occur.

PNNL joint appointee Auroop Ganguly uses data science to study the impacts of climate change.

A new web-based tool provides easy-to-understand progress metrics and other data about groundwater cleanup sites overseen by the DOE Office of Environmental Management.

SEA-CROGS is one of four Mathematical Multifaceted Integrated Capability Centers selected for support by the Department of Energy.

New research makes molecular-level calculations of water faster and easier.

PNNL researchers teamed up with SambaNova to enhance artificial intelligence for scientific computing.

Hypergraphs can more faithfully identify important genes in complex immune responses.