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

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.

Data from increasingly complex mass spectrometry instruments met with adaptable data analysis solution.

Proteomics revealed molecular mechanisms by which the drug Verapamil could prolong beta cell function in early-onset type 1 diabetes patients.

Measurements of forest after an anomalous seawater exposure event reveals carbon starvation as dominant process underlying deaths.

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

Study highlights microbiome’s role in regulating xenobiotic metabolism.

Soil pores are reorganized when newly thawed permafrost soils experience freeze-thaw cycling.

Development of a one-meter path length miniature ion mobility spectrometer based on multilevel structures for lossless ion manipulations.

Study reveals importance of accounting for micrometer scale distribution of substrates to predict carbon emissions from soil.

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.

Researchers characterize protein signatures that indicate Acute Kidney Injury in COVID-19 patients.

Simulations reveal advantages of using new model vs. allometric scaling for risk assessment.

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

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