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.

Nine national laboratories, including PNNL, bring together over 1,000 scientists for a day to use AI to accelerate scientific discovery.

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

PNNL chemical engineer Mariefel Olarte shares passion for mentoring students and the next-generation workforce.

A potential quantum advantage for fluid dynamics simulations from molecular to atmospheric scales with a new formula for success.

PNNL Program Development Office Director Karl Mueller explains how the TEC4 project seeks to make advanced chemical computations accessible to all.

Electronic structure codes in a cloud environment can enable efficient computational chemistry simulations.

CACTUS provides AI-powered cheminformatics for autonomous science.

A new generation of microelectronics research begins at PNNL.

Pacific Northwest National Laboratory researchers developed a new theoretical method for studying highly correlated systems.

Scientists are using artificial intelligence and powerful computing to sculpt new molecules in an effort to treat disease.

Samrat Chatterjee will serve on the editorial board of Risk Analysis, the flagship journal of the Society for Risk Analysis.

How to keep stray radiation from “shorting” superconducting qubits; a pair of studies shows where ionizing radiation is lurking and how to banish it.

PNNL researchers work with industry partners to transform large language models.

University of Washington Professor David Baker won the 2024 Nobel Prize in Chemistry for computational protein design.

The search for dark matter includes expertise in radio frequency signal detection, quantum sensing, and high-energy physics at PNNL.

Calculating quantum chemistry quickly allows scientists to refine their understanding of complex chemical systems.

Pacific Northwest National Laboratory scientists discussed their materials research at Accelerate’24.

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.