PNNL is one of the founding partners of the Trillion Parameter Consortium, which was formed to create reliable generative AI models.

This is the first time a flow battery with 24-hour discharge capacity will be deployed and tested in a field environment.

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.

PNNL celebrates World Energy Storage Day (September 22) by introducing you to some of our best and brightest in this area.

PNNL energy storage experts express need for continued investment in developing and deploying long-duration energy storage.

Methods review suggests ways to use artificial intelligence and machine learning to handle missing data when integrating two or more omics approaches.

Department of Energy Office of Electricity Assistant Secretary Gene Rodrigues makes his first visit to PNNL.

Gosline works to develop computational algorithms that are uniquely targeted for rare disease work by doing foundational research in model system development. This work can be expanded to all model systems in human disease.

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.

Findings show method to better prepare human lung tissue for study; it represents the latest mass spectrometry-based omics advances.

PNNL and Georgia Tech launch a joint institute focused on accelerating security for the critical infrastructure community.

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

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.

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 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.

New quantum chemistry pipeline can help predict the effects of bioengineering.