Scientists are reviewing the current science of the mechanism and structural dynamics of methyl coenzyme-M reductase, an enzyme involved in biological methane conversion.

A team combined experiments and theory to predict the collective properties of electrolyte solutions.

Neural networks techniques enable the efficient capture of electron correlation effects in reduced-dimensionality spaces.

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

The first measurement of the proton diffusion constant at cryogenic temperatures provides insights into the mechanism of proton movement in supercooled water.

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

Directionally biased electrostatic interactions are the key to significantly tuning the reduction potential of iron/sulfur clusters.

The Center for Continuum Computing at PNNL aims to integrate cloud platforms, high-performance computing, and edge devices into a seamless ecosystem that accelerates scientific discovery.

The low-temperature breakdown of polyolefins is driven by carbenium ions generated from chloroaluminate ionic liquids.

Developing a new scheme to more efficiently solve quantum chemistry problems.

A PNNL team has developed an energy- and chemical-efficient method of separating valuable critical minerals from dissolved solutions of rare earth element magnets.

Increasing concentrations of ions slows the rate of particle aggregation in boehmite suspensions.

The structure and exposed surfaces of hematite particles affect their growth and dissolution.

PNNL chemist and AI scientist Nancy Washton invited to present on AI literacy at National Science Bowl.

Surface manganese mining from nodules in South Dakota is now possible with a new scalable selective precipitation process.

Formal mathematics both reveals what’s inside the black box of AI algorithms and pushes the boundary of what’s possible within math as a discipline.

PNNL's “co-scientist” serves as a one-stop AI shop for accelerating scientific discovery. By leveraging AI agents, researchers can explore scientific databases, conduct analyses and request step-by-step plans for testing their hypotheses.

Chemist Wendy Shaw, a nationally recognized scientific leader, has been chosen to serve as the associate laboratory director for PNNL's Physical and Computational Sciences Directorate.

PNNL’s Yong Wang has been elected to the National Academy of Engineering and will be inducted in October.

Researchers have created an algorithm that slashes the calculations necessary to prepare and customize data for quantum computing.