Harnessing the sea for decarbonized energy is the focus of a new collaboration between PNNL and the University of Guam.

Staff at PNNL recently completed a report highlighting commercial products enabled through projects funded by the Department of Energy’s Building Technologies Office.

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

The Simple Building Calculator, developed at PNNL, meets a need for a quick, interactive, and economic method to evaluate energy use—and potential savings from efficiency measures—in simple commercial buildings.

Researchers developed a machine learning model that can analyze chemical reactions as they happen in an electron microscope.

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

Researchers identified a new intermediate phase that forms during the crystallization of hydrated aluminum-containing salts.

PNNL researchers are moving energy research forward through collaborative projects with universities across the United States.

The nature of an iron oxide mineral and amount of light control electron movement from a light-absorbing molecule to the mineral, rather than the conditions of the surrounding aqueous environment.

As leaders in AI and machine learning, PNNL experts are sharing their latest findings at the 36th annual Neural Information Processing Systems (NeurIPS) Conference, Nov. 28–Dec. 9, 2022.

One year into an Early Career Research Program award, Marcel Baer is developing new workflows to model biologically inspired systems more efficiently.

A new project will produce a database of optimized conditions for efficient, carbon negative recovery of energy-relevant minerals in red mud waste.

Data-driven experiments can help determine best practices for Random Forest machine learning to predict water quality, particularly nitrate levels.

PNNL gathered researchers from eight national laboratories plus the U.S. Department of Energy (DOE) to share ideas and build synergy at the Energy Equity and Environmental Justice Summit.

A review article examines a decade of work on the atom-scale process of turning carbon dioxide into stable minerals.

PNNL joint appointee Auroop Ganguly uses data science to study the impacts of climate change.

The amount of radiation in a system influences how fast aggregated boehmite particles dissolve.

A new deep learning approach allows researchers to rapidly analyze three-dimensional representations of multi-component catalysts.

Using electrocatalysis offers a different path for reactions involving polar groups.

Where molybdenum dopants sit within catalytic material affects catalysis at a mechanistic level.