AI helps US fusion lab predict ignition outcomes with 70% accuracy

Scientists at Lawrence Livermore National Laboratory (LLNL) have developed an AI model that predicts the outcome of inertial confinement nuclear fusion experiments with over 70% accuracy, outperforming traditional supercomputing methods. The deep learning model was trained on a combination of previously collected experimental data, physics simulations, and expert knowledge, enabling it to capture complex parameters and replicate real experiment imperfections. When tested on the National Ignition Facility’s (NIF) 2022 fusion experiment, the AI correctly predicted a 74% probability of a positive ignition outcome, demonstrating its potential to optimize experimental designs before physical trials. Nuclear fusion, which combines light atomic nuclei to release energy, promises a cleaner and more efficient energy source than current nuclear fission plants, producing significantly more energy per kilogram of fuel without radioactive byproducts. The NIF uses powerful lasers to induce fusion in tiny fuel capsules, but due to the limited number of ignition attempts possible annually, optimizing each experiment is critical. The AI model’s ability