In a new approach, quantum flow algorithms are used to simulate many-body systems on quantum computers and effectively describe chemical processes.

Researchers devised a quantitative and predictive understanding of the cloud chemistry of biomass-burning organic gases helping increase the understanding of wildfires.

Spatial proteomics enables researchers to link protein measurements to features in the image of a tissue sample, which are lost using standard approaches.

The new PTM-Psi workflow opens the door for uncovering molecular insights within proteomics datasets.

High fidelity simulations enabled by high-performance computing will allow for unprecedented predictive power of molecular level processes that are not amenable to experimental measurement.

A combined experimental and theoretical study identified multiple interactions that affect the performance of redox-active metal oxides for potential electrochemical separation and quantum computing applications.

Data-driven autonomous technology to rapidly design and deliver antiviral interventions targeting SARS-CoV-2 to reduce drug discovery timeline and advance bio preparedness capabilities.

A new approach broadens the reach of quantum computers to previously challenging systems.

Silicone wristbands could be more convenient option for collecting exposure data.

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

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

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

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.

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.

A team of researchers developed a simulation approach to identify how atomic structures can affect the phonon transport of energy and information in quantum systems near absolute zero temperatures.

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

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.