CACTUS provides AI-powered cheminformatics for autonomous science.

PNNL’s Center for Cloud Computing is addressing challenges in data management and hybrid cloud solutions with industry and academic partnerships.

New datasets delineating global urban land support scientific research, application, and policy, but they can produce different results when applied to the same problem making it difficult for researchers to decide which to use.

The demand for energy is growing—and so is the technology supporting it. However, future development of power generation technologies could be affected by a key factor: material supply.

PNNL researchers work with industry partners to transform large language models.

University of Washington Professor David Baker won the 2024 Nobel Prize in Chemistry for computational protein design.

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.

The Delaware River Basin is projected to experience a regional increase in flood severity under future climate scenarios.

ChemReasoner combines decades of chemistry knowledge with large language models to propose strategies for effective new catalysts.

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

PNNL shared recent developments in artificial intelligence at the inaugural Special Competitive Studies Project AI for National Competitiveness Expo.

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.

PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.

New methodological approach demonstrates how to assess the economic value, including non-traditional value streams, of converting non-powered dams to hydroelectric facilities.

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

A PNNL study developed a water management module for Xanthos that distinguishes between the operational characteristics of hydropower, irrigation, and flood control reservoirs.

Pacific Northwest may face increase in summer heat-dome-like stationary waves.

Pacific Northwest National Laboratory launches the Training Outreach and Recruitment for Cybersecurity Hydropower program at the University of Texas at El Paso.

Earth Scientist Laura Fierce mentors Department of Energy Science Graduate Student Research program awardees.

PNNL researchers comment on the “Adversarial Machine Learning” white paper from the National Institute of Standards and Technology.