PNNL's “co-scientist” serves as a one-stop AI shop for accelerating scientific discovery. By leveraging AI agents, researchers can explore scientific databases, conduct analyses and request step-by-step plans for testing their hypotheses.
By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.
PNNL’s year in review includes highlights ranging from advancing soil science to understanding Earth systems, expanding electricity transmission, detecting fentanyl, and applying artificial intelligence to aid scientific discovery.
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.
PNNL and one of the world’s largest tire makers will work to develop a commercially viable process that converts ethanol derived from sustainable sources or waste, like recycled tires, to butadiene, synthetic rubber’s main ingredient.
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.
Battelle Fellow Johannes Lercher was elected a Foreign Academician by the Royal Academy of Exact, Physical, and Natural Sciences of Spain for his contributions to chemical science.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
