The Sodium-ion Alliance for Grid Energy Storage, led by PNNL, is focused on demonstrating high-performance, low-cost, safe sodium-ion batteries tested for real-world grid applications.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
The Grid Storage Launchpad dedication event was attended by leaders in grid and transportation energy storage, battery innovation, and industry stakeholders working to transform America’s energy system.
PNNL will engage with transmission planners and other regional partners through technical assistance and listening sessions with the goal of exploring opportunities to integrate equity into transmission planning.
PNNL staff in the Artificial Intelligence and Data Analytics division were recognized by the TSA’s Innovation Task Force (ITF) for their contributions to cloud capabilities, development strategies, and smart management of cloud resources.
Erich Hsieh, Deputy Assistant Secretary for OE’s Energy Storage Division, shared insights about the Grid Storage Launchpad and energy storage innovations .
PNNL and collaborators developed new models—recently approved by the U.S. Western Electricity Coordinating Council (WECC)—to help utilities understand how new grid-forming inverter technology will enhance grid stability.
A team of researchers from Pacific Northwest National Laboratory and the Environmental Molecular Sciences Laboratory developed a new and flexible software tool called “Advanced Spectra PCA Toolbox.”
The Lab’s newly formed Center for AI, in partnership with NVIDIA, recently hosted a joint “LLM Day.” During the day, NVIDIA AI experts engaged with PNNL scientists on opportunities to make generative AI a powerful tool for science.
A new study demonstrates a hybrid model that can simulate part of a system at the molecular scale and other parts at larger scales in a computationally efficient manner, providing greater simulation flexibility.
At the second Grid Resilience to Extreme Events Summit, a diverse range of experts gathered to tackle the biggest challenges in building a resilient grid.
Scientists at PNNL harnessing advances in deep learning, deep reinforcement learning and generative AI to change how science is conducted and achieve original scientific results and breakthroughs.
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.