Scientists Have Taught AI to Predict Nuclear Fusion Success—and It’s Already Paying Off

Scientists at Lawrence Livermore National Laboratory have developed an AI model that successfully predicted the outcome of a high-stakes nuclear fusion experiment at the National Ignition Facility (NIF), marking a major leap in fusion research. Published in the journal Science, the study reveals that a deep learning system accurately forecasted the 2022 ignition attempt with a 74% probability of success—outperforming traditional supercomputing simulations in both speed and precision. The model, built by researchers in NIF’s Cognitive Simulation Group led by co-author Kelli Humbird, integrates historical experimental data, high-fidelity physics simulations, and expert knowledge to create a comprehensive dataset. After running for over 30 million CPU hours on powerful supercomputers, the AI learned to identify patterns in what can go wrong during fusion experiments—such as laser misfires, imperfections in fuel capsules, or unexpected physical anomalies. NIF’s fusion process relies on laser-driven implosions. Powerful lasers heat a gold cylinder called a hohlraum, which emits X-rays that compress a tiny fuel pellet—about the size of a pea—containing deuterium and tritium. When compressed under extreme conditions, these hydrogen isotopes fuse, releasing vast amounts of energy. The ultimate goal is to achieve ignition: producing more energy than the lasers input. Traditional computer simulations struggle with the complexity of these events. They are often simplified for computational feasibility, which can introduce errors, and they take days to run. Humbird likened the challenge to climbing an uncharted mountain using an imperfect map—one that might be flawed due to design limitations or real-world unpredictability. Each experiment is expensive and time-consuming, with only a few attempts possible per year. The new AI model acts as a smarter, faster guide. Instead of waiting for lengthy simulations, researchers can now use the model to evaluate experimental designs in minutes, adjusting parameters to maximize the chance of success. When applied to the 2022 experiment, the model correctly predicted the outcome, and further refinements to its physics components boosted accuracy from 50% to 70%. What sets this AI apart is its ability to account for real-world imperfections—equipment flaws, human error, or unpredictable physical quirks—rather than assuming ideal conditions. This realism makes it a more reliable tool for decision-making. Humbird emphasized that while progress is exciting, fusion research remains a long-term endeavor. Success isn’t guaranteed, and setbacks are part of the journey. “We shouldn’t be discouraged when we don’t get the results we hoped for,” she said. “Getting 1 megajoule of energy instead of 2 is still a massive improvement over just 10 kilojoules a few years ago.” The breakthrough offers renewed hope that AI could accelerate the path to practical nuclear fusion—a clean, abundant energy source that could transform the world’s energy future.