Thin oxide films play an important role in electronics and energy storage. Researchers in PNNL’s film growth laboratory create, explore, and improve new thin oxide films.
Mitra Taheri served as a co-editor on a special issue of the Materials Research Society Bulletin, which also featured work from Daniel Schreiber and Cindy Powell.
Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.
A PNNL-developed computational framework accurately predicts the thermomechanical history and microstructure evolution of materials designed using solid phase processing, allowing scientists to custom design metals with desired properties.
Cesar Moriel from University of Texas at El Paso will be interning at the PNNL over the summer as part of the Energy Environment Diversity Internship Program.
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.
Data scientist Jung Lee accepted the position of review editor for Frontiers in Computational Neuroscience after serving as a guest editor for a special issue.
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.