Part 2 of 2 Parts (Please read Part 1 first)
     AI can help narrow the range of candidate materials for testing, characterize them based on their properties, and carry out real-time monitoring of those installed in fusion reactors. These capabilities allow the rapid screening and development of radiation-tolerant materials, reducing reliance on current time-intensive approaches.
     AI also offers improved control of the plasma in fusion reactors. As discussed above, a key challenge in magnetic confinement fusion is to shape and maintain the high-temperature plasma within the fusion reactor.
     However, the plasmas in these fusion reactors are inherently unstable. A control system needs to coordinate the tokamak’s many magnets, and adjust their voltage thousands of times per second to prevent the plasma from touching the walls of the vessel. This could lead to the loss of heat and potentially damage the materials inside the fusion reactor.
     Researchers from the U.K.-based company Google DeepMind have utilized a form of AI called deep reinforcement learning to keep the plasma stable and to accurately sculpt it into different shapes. This allows researchers to understand how the plasma behaves under different conditions.
     A team at Princeton University in the U.S. has also used deep reinforcement learning to forecast disturbances in fusion plasma known as “tearing mode instabilities,” up to three hundred milliseconds before they occur. Tearing instabilities are a major form of disruption that can occur, halting the fusion process. They happen when the magnetic field lines within a plasma break and create an opportunity for that plasma to escape the control system in a fusion reactor.
     Work at the U.K. Atomic Energy Authority (UKAEA) is addressing critical challenges in materials performance and structural integrity by integrating a variety of techniques. These include machine learning models, for evaluating what’s known as the residual stress of materials. Residual stress is a way to measure performance that’s locked into materials during manufacturing or operation. It can seriously affect the reliability and safety of fusion reactor components under extreme conditions.
     A key outcome of this work is the development of a technique that integrates data from experiments with a machine learning-powered predictive model to evaluate residual stress in fusion components.
     This new technique has been validated through collaborations with leading institutions, including the National Physical Laboratory and UKAEA’s materials research facility. These developments provide efficient and accurate assessments of materials performance and have redefined the evaluation of residual stress. They have unlocked new possibilities for assessing the structural integrity of components used in fusion reactors.
     This research directly supports the European Demonstration Power Plant (EU-DEMO) and the Spherical Tokamak for Energy Production (STEP) projects. These projects aim to deliver a demonstration fusion power plant and prototype fusion power plant, respectively, to scale. Their success depends on maintaining the structural integrity of critical components under extreme conditions.
     By using many AI-based approaches in a coordinated way, scientists can ensure that fusion reactors are physically robust and economically viable, accelerating the path to commercialization. AI can be used to develop accurate simulations of fusion reactors that integrate insights from plasma physics, materials science, engineering, and other aspects of the process. By simulating fusion systems within these virtual environments, scientists can optimize reactor design and operational strategies.
Spherical Tokamak for Energy Production

Part 1 of 2 Parts
     The pursuit of nuclear fusion as a clean, sustainable energy source is one of the most challenging scientific and engineering goals of our time. Commercial fusion power promises nearly limitless energy without carbon emissions or long-living radioactive waste.
     However, achieving practical fusion power requires solving significant challenges. These challenges come from the heat generated by the fusion process, the radiation produced, the progressive damage to materials used in fusion reactors, and other engineering problems. Fusion systems operate under extreme physical conditions, and they generate data at scales that surpass the ability of humans to analyze.
     Nuclear fusion is the process that powers our Sun and other stars. Existing commercial nuclear power relies on a process called fission, where a heavy chemical element nucleus is split to produce lighter ones. Nuclear fusion works by combining the nuclei of two light elements to make a heavier one.
     While physicists have been able to initiate and sustain fusion for short periods of time, getting more energy out of the process than the energy supplied to power the fusion device has been a serious challenge. So far, this has prevented the commercialization of this hugely promising energy source.
     Artificial intelligence (AI) is emerging as a powerful and critical tool for managing the challenges of fusion research. It holds promise for dealing with the complex data and convoluted relationships between different aspects of the fusion process. This development not only enhances our understanding of fusion but also accelerates the development of new reactor designs.
     By addressing these challenges, AI offers the potential to significantly compress timelines for the development of fusion reactors. It will pave the way for the commercialization of nuclear fusion.
     AI is reshaping fusion research across academic, government, and commercial sectors which is driving innovation and progress toward a sustainable energy future. It can play a transformative role in addressing the challenges of developing materials for fusion reactors. These new materials must be able to withstand extreme thermal and neutron environments while maintaining structural integrity and functionality.
     By connecting datasets from different experiments, simulations, and manufacturing processes, AI models can generate predictions and insights that can be acted on. Machine learning is a form of AI that can significantly accelerate the evaluation and optimization of materials that could be used in fusion reactors.
     These include the doughnut-shaped fusion reactors called tokamaks used in magnetic confinement fusion (where magnetic coils are used to guide and control hot plasma, allowing fusion reactions to occur). The extremely hot plasma can damage the materials used in the interior walls of the tokamak, as well as irradiating them.
     Machine learning involves the use of algorithms (a set of mathematical rules) that can learn from experimental data and apply those lessons to unseen problems. Insights from this form of AI are extremely critical for guiding the selection and validation of materials able to endure the harsh conditions inside fusion reactors. AI has allowed scientists to develop detailed simulations that permit the rapid evaluation of materials performance and their configurations within a fusion reactor. This approach helps ensure long-term reliability and cost efficiency.
Fusion Reaction
Please read Part 2 next