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

Microbes and viruses play key roles in carbon and nitrogen cycling in riverbed sediments.

Proteins from “auxiliary” genes in soil viruses play a role in the carbon cycle and climate, scientists have found.

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.

A process developed at PNNL that converts biomass and waste into a chemical intermediate or into gasoline, diesel, and jet fuel is available for commercial licensing.

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.

Forms of nitrogen released by nitrogen-fixing bacteria differ across sampling scales and in structured environments.

Dominant and functionally important soil microbes show strong, predictable, and distinctly different associations with continental-scale gradients in climate, vegetation, and soil moisture.

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.

A new perspective article discusses how integrating carbon dioxide capture and conversion in solvents can lead to cheaper and more efficient carbon management systems.

Providing a perspective on the importance of integrating experimental work with theory and simulations when designing new carbon capture solvents.

Soil transplant experiment gives new insights into coastal forest resilience, rising seas, and increasing storms.

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

A novel ecological measurement uncovered interactions between river corridor organic matter assemblages and microbial communities, highlighting potentially important microbial taxa and molecular formula types.

PNNL scientists have developed a new mass spectrometry method for in-depth analysis of proteins in individual cells.

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