New research makes molecular-level calculations of water faster and easier.

A new study shows how organic molecules respond to global change varies according to functional traits and ecological processes.

Updated flexible software generates and optimizes monitoring programs for detecting potential leaks from geological carbon storage with an enhanced user experience.

PNNL researchers teamed up with SambaNova to enhance artificial intelligence for scientific computing.

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

A novel combination of temperature and four-dimensional geophysical sensors revealed otherwise unseeable hydrologic dynamics below riverbed sediments.

PNNL scientists have proposed an "adaptive site management" cleanup strategy for the Hanford Site's Central Plateau that incorporates a structured, flexible approach to environmental remediation.

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.

ICON science is a Department of Energy-developed framework to enhance scientific outcomes via more intentional design of research efforts across all domains of science.

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

New data analysis approach gives researchers deeper understanding of microbial ecology and evolution.

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.

Improving predictability of water quality and nutrient cycling in large river systems.

Developing conceptual models for microbial-environmental–ecosystem interactions is key to enhancing the ability of models to predict future ecosystem function.

Research from the PNNL-led WHONDRS consortium reveals chemistry and spatial gradients of metabolomes from river water and sediments around the globe.

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

Detailed characterization of molecules in, under, and along the Columbia River reveal potential mechanisms for biogeochemical cycling.

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