Scientists have tapped AI and powerful computing to speed up how quickly officials are able to learn important details about nuclear events.

Researchers from PNNL and Parallel Works, Inc., applied machine learning methods to predict how much oxygen and nutrients are used by microorganisms in river sediments.

This study presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations.

Chemist Wendy Shaw, a nationally recognized scientific leader, has been chosen to serve as the associate laboratory director for PNNL's Physical and Computational Sciences Directorate.

Researchers can now quantify uncertainty in trained AI models aimed at solving difficult chemistry, biology and materials science problems.

Researchers have created an algorithm that slashes the calculations necessary to prepare and customize data for quantum computing.

Mini flow cell battery provides the first step toward an AI-driven, robotic energy storage discovery laboratory.

Federal environmental permitting process to be fast-tracked with AI-powered tools and cloud-supported data analysis.

Computing and artificial intelligence aid scientists who seek to understand function-driven design and control of biological systems.

The speed and agility of cloud computing opens doors to completing advanced computational chemistry workflows in days instead of months.

Three PNNL technologies have been named 2024 R&D 100 Award winners.

A team of researchers from Pacific Northwest National Laboratory and the Environmental Molecular Sciences Laboratory developed a new and flexible software tool called “Advanced Spectra PCA Toolbox.”

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.

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.

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.

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

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

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