The Institute for Integrated Catalysis (IIC) at Pacific Northwest National Laboratory explores and develops the chemistry and technology of catalyzed processes that enable a carbon-neutral future.
IrrigationViz is a visual decision-support tool that provides users with high-level estimates for irrigation modernization projects, such as concrete lining for a canal or replacing a canal with a pipeline.
The Isotope Program at PNNL supports scientific advances in the production and use of radioisotopes for research, medicine, and industrial applications.
PNNL is heavily engaged in the development and use of mass spectrometry technology across its science, energy, and security missions, from fundamental research through mature operational capabilities.
Advancing the understanding and monitoring of nuclear material processing to accelerate development and qualification of new material systems for national security and nuclear energy.
Mega AI seeks to develop massive-scale, self-supervised, multimodal foundation models of scientific knowledge capable of general-purpose inferences to enable reasoning with existing knowledge and discovery of new knowledge.
The NNSA Graduate Fellowship Program is administered by Pacific Northwest National Laboratory and sponsored by the NNSA to provide students with training and practical experience that achieve the NNSA mission.
Physics-informed machine learning (PIML) is a modeling approach that harnesses the power of machine learning and big data to improve the understanding of coupled, dynamic systems.
PNNL data scientists and engineers will be presenting at NeurIPS, the Thirty Fourth Conference on Neural Information Processing Systems, and the co-located Women in Machine Learning workshop, WiML.
The user-friendly Project Schedule Visualizer software developed at PNNL helps users readily identify and understand the impacts of updates to the schedule, budget, and risks associated with large, complex projects that cross departments.
PNNL combines AI and cloud computing with damage assessment tools to predict the path of wildfires and quickly evaluate the impact of natural disasters, giving first responders an upper hand.
The RD2C laboratory-directed research initiative seeks to develop resilient, adaptive, and intelligent sensing and control algorithms through the observational understanding and characterization of CPSs under adverse conditions.
Powered by few-shot learning, the Sharkzor AI-driven, scalable web application makes it possible to quickly characterize and sort electron microscopy images used to analyze radioactive materials.
STOMP is a suite of numerical simulators for solving problems involving coupled flow and transport processes in the subsurface. The suite of STOMP simulators is distinguished by application areas and solved mathematical equations.
PNNL has developed a tool suite of interactive analytics that can be rapidly integrated into analyst workflows to empirically analyze and gain qualitative understanding of AI model performance jointly across dimensions.
UTEP and PNNL are advancing the collective scientific impact of both institutions through collaborations between PNNL researchers and UTEP faculty, as well as by building on the complementary strengths to grow a diverse STEM workforce.