Researchers at PNNL are sharing work grounded in One Health, which connects human, animal, plant, and environmental data to improve situational awareness, early warning, and decision-making.

Separating quantum circuit wiring from molecular data allows researchers to more efficiently perform quantum simulations

Exploring how shifting boundaries between upland and wetland areas influence soil processes and signal early ecosystem transformations.

A new report highlights a public workshop hosted by the National Academies Forum on Microbial Threats and features PNNL's Lauren Charles and the AI-Driven One Health Security program.

Integrating new functions into a genome-scale model enabled researchers to simulate and experimentally validate viral hijacking pathways.

A suite of new modeling methods gives researchers better tools to predict molecular behavior inside cavities.

A recent article highlighted a PNNL report focused on post-quantum cryptography and electric vehicle charging infrastructure.

Darío Gil, DOE's Undersecretary for Science, makes his inaugural visit to PNNL.

An approach combining multiple theories increases the accuracy of quantum-based simulations without increasing computational demands.

PNNL scientist James Stegen and an international team of collaborators recently published a comprehensive review of variably inundated ecosystems (VIEs).

Neural networks techniques enable the efficient capture of electron correlation effects in reduced-dimensionality spaces.

In the search for rare physics events, extremely pure materials are essential. A partnership between PNNL and Ultramet has led to tungsten with low contamination from other elements.

Developing a new scheme to more efficiently solve quantum chemistry problems.

This project sought to assure that research activities centered around different sampling and monitoring efforts in northwest Ohio would not disturb any historical cultural resources.

Pacific Northwest National Laboratory researchers developed a new theoretical method for studying highly correlated systems.

Nineteen PNNL-affiliated researchers have been named to Clarivate’s 2024 list of the world's most highly cited researchers.

In a recent publication in Nature Communications, a team of researchers presents a mathematical theory to address the challenge of barren plateaus in quantum machine learning.

A new approach validates quantum algorithms for low-energy nuclear physics applications

Research at PNNL and the University of Texas at El Paso are addressing computational challenges of thinking beyond the list and developing bioagent-agnostic signatures to assess threats.

A new quantum algorithm speeds up simulations of coupled oscillator dynamics.