Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.

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.

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.

Researchers developed a machine learning model that can analyze chemical reactions as they happen in an electron microscope.

Researchers identified a new intermediate phase that forms during the crystallization of hydrated aluminum-containing salts.

As leaders in AI and machine learning, PNNL experts are sharing their latest findings at the 36th annual Neural Information Processing Systems (NeurIPS) Conference, Nov. 28–Dec. 9, 2022.

PNNL research, featured on the cover of two science journals, describes advancements in using Raman spectrometry for Hanford Site nuclear waste remediation.

PNNL researchers teamed up with SambaNova to enhance artificial intelligence for scientific computing.

PNNL researchers design an artificial intelligence-guided electron microscope in a Laboratory Directed Research and Development study.

Combining multiple experimental techniques allowed researchers to observe the early stages of oxidation in a model material.

PNNL scientists developed new capabilities to study materials under extreme conditions.

Different sized helium cavities that form after ion irradiation are unevenly distributed in a ductile-phase toughened tungsten composite.

IDREAM researchers assess the potential of photon-in/photon-out XFEL techniques to explore early time reaction steps and ultimately improve nuclear waste processing strategies.

A multi-institutional team has developed a deep learning framework to identify novel molecules as drug candidates.

New PNNL research provides the first direct evidence that amorphous intermediates influence subsequent crystallization outcomes using in situ imaging.

A strengthened softer alloy phase increases the toughness of a tungsten composite material.

New study elucidates the complex relaxation kinetics of supercooled water using a pulsed laser heating technique at previously inaccessible temperatures.

Researchers developed a new tool that combines artificial intelligence and microscopy data to rapidly test new materials for fusion reactor use.

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.