The PNNL-patented ShAPE™ manufacturing process produces high-strength aluminum vehicle parts that lower costs and are more environmentally friendly.

Advances in understanding the nature of the chemical bond are featured in this special issue of The Journal of Chemical Physics.

Vrabie co-organized the 2022 Workshop on Artificial Intelligence for Isotope R&D and Production for the Department of Energy Isotope Program.

Juan Carlos Idrobo, a joint appointee with the University of Washington, was elected a fellow of the Microscopy Society of America.

PNNL’s Sameera Horawalavithana joins the journals IEEE Transactions on Artificial Intelligence and Humanities and Social Sciences Communications.

Deep learning helps make predictions on the effects of the El Niño-Southern Oscillation on river flows more accurate.

Materials science postdoctoral researcher Jingshan Du received a Distinguished Student Program award to participate in the American Physical Society meeting.

PNNL welcomes new joint appointments to expand the research productivity and scientific impact of both PNNL and the university partners, broadening the base of expertise at each institution and helping to build interdisciplinary teams.

Xantheas was one of four professors in 2021/2022 to be selected by the Göttingen Academy of Sciences and Humanities for this award.

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

PNNL senior materials scientist Keerti Kappagantula has been named to the 2023 Nominating Committee of ASM International.

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.

Combining peptoids and carbon nanotube porins creates a new class of stable membranes with potential applications in separations science.

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

A combined experimental and modeling study shows that electronic interactions direct how a metal-organic framework grows.

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.

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.

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.