Three research teams led by PNNL organizers achieve journal publications following the Mathematics Research Communities program.

Researchers developed a way to precisely control dopant structures and locations in carbon-based materials.

Expanding the nation’s largest wind power plant time series database; helping grid planners navigate a resilient energy future with better data.

Researchers developed a pathway for the solution synthesis of core–crown heterostructures with controlled electronic properties.

Under an optical field, nanoparticle attachment in a model system heavily depends on surface roughness.

PNNL researchers have published their paper, “Introducing Molecular Hypernetworks for Discovery in Multidimensional Metabolomics Data,” in the Journal of Proteome Research.

Chromium cations better adapt to changes in oxygen stoichiometry than iron cations.

Using interfacial charge transfer to control cation charges in oxide superlattices offers insights for designing functional heterostructures.

Calcium carbonate mineralization occurs through a multi-step process that begins with a dense liquid phase.

A newly funded project will develop the fundamental understanding of phenomena that underpin material properties important for quantum applications.

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

Fully identifying what controls nanocrystal assembly requires incorporating non-traditional forces into models.

Combining X-ray-based techniques allows researchers to locate and characterize small concentrations of electrons in complex semiconductor films.

Elias Nakouzi brings 3D atomic force microcopy expertise to a project focused on understanding how hybrid bio-based nanomaterials assemble.

Researchers created a methodology to design custom network topologies that improve the execution time for scientific applications.

The pathway of oxygen ion movement can be controlled based on the initial structure and substrate of strontium iron oxide thin films.

Researchers unveil user-friendly software to keep complex projects on budget and on schedule.

PNNL researchers present challenges and opportunities for fusing graph neural networks with deep reinforcement learning.

PNNL researchers demonstrated a simple method to create stable, identical nanoparticles of PdTe2-like composition, which is known to be superconducting, on a WTe2 TMD support.

PNNL researchers outline open problems in different scientific areas in the Journal of Combinatorics.