A new iron-based aqueous flow battery shows promise for grid energy storage applications.

PNNL scientists have put forth a new approach to protect the electric grid, creating an approach that sorts and prioritizes cyber threats on the fly.

A PNNL study developed a water management module for Xanthos that distinguishes between the operational characteristics of hydropower, irrigation, and flood control reservoirs.

ChatGrid is a practical application of the Department of Energy’s exascale computing efforts and offers a new experience in easy, intuitive, and interactive data interaction.

Results of the PR100 study outline near- and short-term strategies for Puerto Rico to achieve a renewable power grid by 2050.

Machine learning methods and data science can uncover hidden patterns in nanoparticle synthesis conditions and property outcomes.

The convergence of artificial intelligence, cloud, and high-performance computing to accelerate scientific discovery is the focus of a multi-year collaboration between Microsoft and PNNL.

PNNL has created the Center for AI @PNNL to coordinate the pioneering research of hundreds of scientists working on a range of projects in artificial intelligence.

Understanding the risk of compound energy droughts—times when the sun doesn’t shine and the wind doesn’t blow—will help grid planners understand where energy storage is needed most.

Input from humans helps when deciding whether to trust the recommendations or decisions of a machine-learning system.

Floating offshore wind farms could potentially triple the Pacific Northwest's wind power capacity while offsetting billions of dollars in costs for utilities, ratepayers, insurance companies, and others.

PNNL is collaborating with Microsoft, Micron and other partners to make computational chemistry broadly available.

A new flow battery design achieves long life and capacity for grid energy storage from renewable fuels.

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

Hydropower generation estimated using water proxy data leads to a revision of the nation’s historical seasonal hydropower output.

PNNL researchers discovered a faster, more economical method to verify the amount of biofuel in mixtures of bio and fossil fuel.

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.

Scientists have taken a key step toward harnessing a form of AI known as deep reinforcement learning to protect computer networks.

A new sodium battery technology shows promise for helping integrate renewable energy into the electric grid. The battery uses Earth-abundant raw materials such as aluminum and sodium.

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.