Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.

Integrating new functions into a genome-scale model enabled researchers to simulate and experimentally validate viral hijacking pathways.

A suite of new modeling methods gives researchers better tools to predict molecular behavior inside cavities.

Secretary Wright visited PNNL's campus in Richland, Washington, late last year to meet with innovators in chemistry, biology, computing, and more.

Darío Gil, DOE's Undersecretary for Science, makes his inaugural visit to PNNL.

Now, anyone can easily explore and access data from a nationwide map of data centers, the infrastructure that powers them, and projections of future data center locations.

The Scientific Discovery through Advanced Computing institute will focus on making scientific machine learning accessible to domain scientists.

AI for Good event commemorates 60 years of community engagement, features AI applications, and offers practical AI tools to empower people.

PNNL researchers developed a new computational workflow to study day-to-night gene expression dynamics of cyanobacterium in real time.

This achievement earned DIMPLES a Best Paper award at the ACM International Conference on Supercomputing 2025.

More than 25,000 participants from industry, government, academia, and the public attended the event.

A new time-dependent wavefunction formulation for the cumulant Green’s functions can produce more accurate simulations.

Neural networks techniques enable the efficient capture of electron correlation effects in reduced-dimensionality spaces.

Developing a new scheme to more efficiently solve quantum chemistry problems.

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

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.

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

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