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

Hydrogen bonding and proton transfer control the identity and reactivity of molecules in highly concentrated and alkaline aluminate solutions.

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.

Developing a new understanding of the structure of natrophosphate, a complex mineral found in radioactive tank waste at the Hanford Site, by integrating experimental techniques.

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.

The size of the alkali metal within the material influences the crystallization pathway.

Researchers in the IDREAM Energy Frontier Research Center unearth new defects during gibbsite crystallization using a multi-instrument approach.

Newly determined energy transfer mechanisms help identify how water releases from and damages irradiated interfaces.

Developing an understanding of how water breaks apart after radiation exposure.

IDREAM researchers use fast force mapping to provide insights into the boehmite interfacial solution structure.

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.

IDREAM study characterizes chemical species and mechanisms that control aluminum salt and mineral crystallization for nuclear waste retrieval, processing.

Researchers gained insight into the interfacial radiation chemistry of radioactive waste sludge through studies of surface functional groups on model aluminum-containing solids