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.

PNNL researchers design an artificial intelligence-guided electron microscope in a Laboratory Directed Research and Development study.

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

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

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

Researchers achieved the complete assignment of the infrared spectrum of a hydronium-water “magic number” cluster reported in Science in 2004.

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

A multi-institutional team has developed a deep learning framework to identify novel molecules as drug candidates.

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

Scientists created a benchmark dataset and explored new algorithms to better predict changes in the intensity of North Atlantic tropical cyclones.

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.

New research uncovers the mechanism of carbon dioxide reduction by metal-O-Fe bonds of single-metal atoms and metal nanoparticles supported by oxidic surfaces.

Researchers used deep learning methods to estimate the subsurface permeability of a watershed from stream discharge measurements.

Characterizing the electron redistribution that allows the binding of molecular nitrogen to the catalytic core of nitrogenase.