Under Secretary for Science and Innovation at the Department of Energy Geri Richmond visited PNNL.

Irradiated boehmite nanoplatelets form smaller aggregates and have a rougher surface than non-irradiated platelets.

Water affects the intermediates present on the surface of a metal oxide catalyst during ketonization.

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

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

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.

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

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

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.

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.

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.

Within zeolite pores, rates of alcohol dehydration increase with ionic strength until reaching a maximum and then decreasing.

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.

In a water-containing system, the open circuit potential stabilizes positively charged species and lowers the energy barrier of the reaction.

A review article led by researcher Jade Holliman explores the different classes of metamaterials, from the underlying fundamental science to potential applications.

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

PNNL researchers are moving energy research forward through collaborative projects with universities across the United States.