A new simple and scalable synthesis produces nanoparticle assemblies that can perform catalytic hydrogen sensing at room temperature for the first time.

A study that combined experiments and theory showed how model enzymes control challenging reactions involving highly reactive carbocations.

New research from PNNL results in faster and more accurate variational quantum algorithm training.

By decreasing the amount of precious metal in a catalyst, the system increased its efficiency and selectivity for breaking apart plastics.

PNNL researchers developed a hybrid quantum-classical approach for coupled-cluster Green’s function theory that maintains accuracy while cutting computational costs.

PNNL researchers created a model that helps materials scientists not only predict what is, but what could be.

The Co-Optimization of Fuels & Engines initiative recently outlined six years of research in a findings and impact report.

Tuning the environment and platinum-group metal allows researchers to control which bonds the catalyst breaks using hydrogen.

Researchers use machine learning to speed up the analysis of aerosol datasets from mass spectrometry.

Quantum simulators help navigate noise on GPU-based HPC systems.

PNNL researchers utilized the Peeters-Devreese-Soldatov formulation to improve the accuracy of calculations for quantum chemistry.

A comprehensive understanding of the electronic structure of uranyl ions provides insight into the chemistry of nuclear waste and uranium separation technologies.

PNNL researchers worked to decouple the effects that solvents impose on reactions catalyzed in the liquid phase.

New research finds that reaction rates for cyclic alcohol dehydration increase as ionic strength increases, independent of reaction mechanism.

PNNL researchers developed a way to produce accurate simulations of complex physical systems using renormalized approximations across multiple scales

Hypergraphs can more faithfully identify important genes in complex immune responses.

The web-based tool, called PLATIPUS, is publicly available and interacts with publications available in the CovidScholar database.

Seeking a deeper understanding of when, where, and why large wildfires occur using explainable artificial intelligence.

Local ionic strength within zeolite pores tailors the chemical potential of reacting molecules, leading to high cyclohexanol dehydration activity

Tetranuclear molybdenum sulfide clusters encaged in zeolites mimic the FeMo-cofactor of nitrogenase, offering a new opportunity for improving industrial hydrotreatment processes.