PNNL is helping communities with significant historical ties to fossil energy understand opportunities and pursue numerous federal resources available to support coal power plant redevelopment.
The Data-Model Convergence (DMC) Initiative is a multidisciplinary effort to create the next generation of scientific computing capability through a software and hardware co-design methodology.
The E-COMP Initiative is creating new capabilities that enable the optimized design and operation of energy systems subject to multiple objectives and with high levels of power electronic (PEL) driven devices.
PNNL is a leader in the integration of aberration-corrected electron microscopy, in-situ techniques, and atom probe tomography to address challenges in nuclear materials, environmental remediation, energy storage, and national security.
PNNL’s ESMI is a Laboratory-funded research and development (R&D) program focused on transforming and accelerating materials development processes for next-generation energy storage technologies.
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.
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 Pacific Northwest National Laboratory is developing a Port Electrification Handbook—a reference to aid maritime ports nationwide in their clean energy transition.
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.
National laboratories, industry and academia are collaborating to provide electric vehicle manufacturers with batteries that are more reliable, high-performing, safe, and less expensive.
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.
