AI is transforming particle accelerator operations and design.
ORNL is developing AI-powered digital twins for the Material Plasma Exposure eXperiment (MPEX) to accelerate the discovery and qualification of materials capable of withstanding the extreme conditions inside future fusion power plants.
ORNL researchers demonstrated the first autonomous pulsed laser deposition (PLD) platform, combining AI, large language models and self-driving experimentation to rapidly synthesize and evaluate advanced thin-film materials.
ORNL researchers are using AI and machine learning to accelerate nuclear physics analyses, reducing experimental time and costs while improving the speed and accuracy of measurements critical to energy, national security, and scientific discovery.
What if a 3D printer could catch its own mistakes — and tell you instantly whether a part is good to use? Peregrine is an AI-enabled technology that brings greater intelligence to metal additive manufacturing.
ORNL’s Advanced Plant Phenotyping Laboratory and Frontier exascale supercomputer provide the ideal environment to apply AI for enhancing plant uptake of critical minerals using engineered proteins and microbes from national laboratory partners
New AI tools are transforming global weather data into fast, high-resolution water forecasts that support energy systems. This approach delivers near real-time insights to strengthen grid reliability, infrastructure resilience, and water management.
AI is connecting plant genetics to real-world traits to better understand and improve photosynthesis. This work helps accelerate the development of more productive energy crops and supports the future of biotechnology.
AI is speeding up complex Earth system modeling, turning days of computation into just hours. Faster predictions help improve decision-making for energy systems and reduce uncertainty in how natural systems behave.
Summary: Automation and autonomy can enable revolutionary scientific advances by coordinating a diverse array of experimental and computational capabilities more efficiently and more effectively than current hands-on approaches.