PNNL's “co-scientist” serves as a one-stop AI shop for accelerating scientific discovery. By leveraging AI agents, researchers can explore scientific databases, conduct analyses and request step-by-step plans for testing their hypotheses.

By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.

A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.

In a new approach, quantum flow algorithms are used to simulate many-body systems on quantum computers and effectively describe chemical processes.

Research from PNNL and the University of Washington demonstrates the extension of the MBE for periodic systems and its use to decompose the lattice energies of different ice polymorphs.

Artificial intelligence may help humans and computers work together to monitor materials that could be used to make weapons.

Exploring why some brains are more resilient to the impact of Alzheimer’s disease could lead to improved diagnoses, treatment and prevention.

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.

PNNL researchers developed a hybrid quantum-classical approach for coupled-cluster Green’s function theory that maintains accuracy while cutting computational costs.

PNNL researchers created a model that helps materials scientists not only predict what is, but what could be.

Quantum simulators help navigate noise on GPU-based HPC systems.

PNNL researchers utilized the Peeters-Devreese-Soldatov formulation to improve the accuracy of calculations for quantum chemistry.

PNNL researchers developed a way to produce accurate simulations of complex physical systems using renormalized approximations across multiple scales

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

The web-based tool, called PLATIPUS, is publicly available and interacts with publications available in the CovidScholar database.

Seeking a deeper understanding of when, where, and why large wildfires occur using explainable artificial intelligence.

New mathematical tools developed at PNNL hold promise to transform the way we operate and defend complex cyber-physical systems, such as the power grid.

Predictive models indicate cells regulate enzyme activity to maintain liquid center.

Contributions from researchers across Pacific Northwest National Laboratory (PNNL) were recently recognized in the preliminary findings of a Secretary of Energy Advisory Board (SEAB) report.

July in the Tri-Cities usually brings sunny skies, hot weather and high demand for electricity as many of us retreat to air-conditioned homes and offices.