As organizations around the world race to harness the power of nuclear fusion, advancements in computer technology may help them over the finish line.     Computational tools like artificial intelligence (AI), augmented and virtual reality (AR/VR), and digital twins are already showing their immense potential in reshaping how scientists and engineers tackle the four challenges common to nuclear fusion projects: finding materials that can resist high neutron flux, achieving steady-state operation*, managing extreme heat flux, and optimizing the fusion fuel cycle.No longer merely hypothetical, these tools are being actively explored by both the private sector and public research programs—each sparking new insights that accelerate progress.Speeding development in the private sectorAt ITER’s 2025 Private Sector Fusion Workshop in April, one message came through loud and clear: new computing technologies may be a game changer for fusion research and development. These advances are poised for use cases across the board—from materials science and plasma control to software engineering and hardware design. They are even helping with mundane office tasks and IT support."AI is fundamentally changing our world in ways which are unimaginable," said Kenji Takeda, Director of Research Incubations at Microsoft Research during his talk. Takeda highlighted how generative AI models, like Microsoft's MatterGen, help discover new materials in other scientific domains by generating molecular structures that meet requirements specified through prompts, much like ChatGPT can be asked to write a poem. Then AI emulators, such as Microsoft’s MatterSim, can be used to simulate how those materials behave under different test conditions thousands of times faster than existing computational tools. This approach could save years in experimental trial and error, and Microsoft demonstrated its usefulness by designing new materials with a bulk modulus exceeding 400 gigapascals—beyond the reach of most traditional screening methods. "This is how we begin to speak the language of nature," said Takeda. "From electrons to cells to the entire planet—AI can help us model and understand these complex systems.”Microsoft’s contribution underscores a broader trend: industry players are applying AI not just to theoretical research but to real engineering workflows. Moreover, many are finding that what works in one domain is very likely to be useful in others.At the ITER workshop, Thea Energy’s CTO David Gates described how digital twins and parametric CAD simplify the design of fusion reactors by generating vessel structures from plasma geometries with precise tolerances. Gates emphasized the value of machine learning—particularly physics-informed models that use real-world laws to predict behaviour and guide system control. "It’s about using actuators and sensors in harmony to stabilize plasma in real time," he said.Complementing these efforts is the emergence of AI-native platforms that combine multiple modalities of reasoning. Arena, a New York-based AI startup, developed a platform called Atlas, an AI-native system that combines physics-based reasoning with large language models. Already used to streamline engineering processes at AMD, Atlas helps design, debug, and optimize complex systems. Mike Frei, General Manager and Hardware Product Lead at Arena, made the case that the same platform can speed up development in fusion projects. Two things, he said, help AI models power faster hardware innovation. One is complex human-like reasoning. Models, like those that power Atlas, follow step-by-step reasoning processes to solve intricate problems. “Some of them do quite well on PhD level questions in subjects like math and physics, and some are already expert coders that can be compared with the best in the world,” Frei noted.The second ingredient is multimodality. “A lot of context needs to be captured that isn't covered in just a simulation and isn’t simple electrical engineering,” said Frei. Atlas combines sensor data, schematics, and software instructions into a single AI interface. It integrates with lab instruments, generates firmware, and learns from every interaction to assist with future tasks.  At the second ITER/Private Sector workshop in April 2025, the opening session was dedicated to the use of artificial intelligence, augmented and virtual reality, and digitalization in fusion science and technology, and how these techniques are accelerating progress in both the public and private sectors. Streamlining work in the public sectorThe impact of AI is not limited to startups and private-sector experiments. At ITER, the world’s largest fusion project, these technologies are being applied across domains—from administrative to operational, and from engineering to scientific.ITER recently digitized its extensive knowledge base—over a million documents—using a fine-tuned OpenAI model. Engineers now access this wealth of expertise via a chatbot trained on internal content, enabling a more natural interface and more intuitive ways of querying the knowledge database.Microsoft's Copilot and GitHub Copilot have already been deployed at ITER, accelerating software development and troubleshooting. AI has been used to inspect welds on ITER’s massive tokamak vessel using computer vision, further ensuring structural integrity. Even the IT help desk has been overhauled with AI to resolve support tickets, allowing engineers to focus on more impactful work.The effects of AI are propagating like a chain reaction across the surrounding ecosystem. Just down the road in Cadarache, the CEA WEST fusion research centre has also adopted AI-powered strategies. According to Xavier Litaudon, research director at CEA, researchers at CEA WEST have trained AI systems to automatically detect, during operation, hot spots on infrared images of the first wall, reducing the risk of damages on the plasma-facing components and helping to achieve longer plasma duration in safe and more stable conditions. â€œWe applied this same approach to ITER simulations,” he said during his presentation about his organization’s recent series of groundbreaking fusion experiments, culminating in a 22-minute-long pulse. AI improves operational safety, reproducibility, reliability and performance by learning from historical pulse behaviour.AI will play an important role once experimental data is produced by ITER plasmas. According to Alberto Loarte, head of the ITER Science Division, AI will be used to provide consistency checks of acquired data—and as the data is being produced it can provide a higher fidelity interpretation of the measurements than what can be achieved through traditional schemes. As the database of information from real experiments at ITER grows, AI will be used to explore different ways of improving plasma performance.“But we do not need to wait until ITER produces experimental results,” says Loarte. “AI is being used today to generate fast and accurate models of physics processes that allow a high-fidelity description of ITER plasma behaviour with a quick turnaround time. These AI-based models can be used to develop a full digital twin of ITER both for the engineering systems as well as for the plasma scenarios and the associated physics processes, which will be used to simulate ITER operation as a complete and realistic system.”ITER recently developed a live digital twin of its plant, integrating drone imagery, 3D scans, and engineering documentation. This immersive model is accessible via tablets and VR headsets, allowing engineers to compare real-time construction with design and flag deviations instantly. Taken together, these examples illustrate how computing tools—whether machine learning, generative AI, or immersive modelling—are streamlining work at every level. The consensus is clear: computing tools are making fusion research more agile, collaborative, and precise. Fusion may still be a long way from lighting homes, but with AI and other digital technologies, it might get there faster. *Steady-state operation: producing fusion power for an unlimited amount of time with high fusion gain. 

Another 110-tonne central solenoid module is on its way to ITER. A little more than 15 years ago, in March 2010, the US Domestic Agency signed a Procurement Arrangement with the ITER Organization for the supply of the central solenoid magnet system. The scope of the Arrangement included the supply of six modules for the central solenoid stack, a seventh module as a spare, a precompression and support structure for the magnet tower, specialized assembly tooling, and busbar and cooling connections.Out of that list of deliverables only three modules remain to be delivered and, since April, one of those has been travelling. After being transported from its production site at General Atomics in Poway, California, to the port of Houston, the fifth module has now been loaded onboard an oceangoing vessel for transport to ITER where it is expected later this month. The sixth and final production module completed testing in May; when it arrives at ITER later this year the assembly teams on site will be able to complete the stack and begin installing the support structure. The seventh central solenoid module, to be used as a spare, will be completed at a later date.

At its 32nd meeting in May, the ITER Council Science and Technology Advisory Committee (STAC) reviewed the fully detailed version of the ITER Research Plan as well as associated blueprints for magnet coil testing and the development of ITER’s neutral beam injection heating system.  Two documents outline how the ITER project plans to achieve its mission and goals—the ITER Baseline, which details the scope, schedule, cost and risk of the project, and the ITER Research Plan, which is the document that guides the commissioning and experimental exploitation of the ITER device once construction is complete. The documents are closely linked, meaning that changes to one must be reflected in the other.For three days in May, the participants to STAC-32 reviewed the latest comprehensive version of the ITER Research Plan, which reflects the commissioning and operation plans of Baseline 2024. They congratulated the ITER Organization and the ITER Members for developing the plan down to a considerable level of detail that also includes, as annexes, plans for the ITER Neutral Beam Test Facility, where ITER neutral beam injection is being tested in advance of operation, and plans for research in the Test Blanket Module Program, where ITER will experiment with tritium production inside the vacuum vessel. Following its review, STAC endorsed the ITER Research Plan and noted that the ITER Organization and the ITER Members’ experimental, theory and modelling programs have made considerable progress in helping the research plan to mature. STAC members visiting the ITER Neutral Beam Test Facility (NBTF) in Padua, Italy, in May 2025. The NBTF Research Plan, which is designed to address any remaining challenges on the road to achieving ITER requirements for neutral beam injection, was endorsed by the 32nd meeting of the STAC. STAC-32 also reviewed the plans for the cold testing of ITER magnets on the Magnet Cold Test Facility test bench, whose assembly is progressing well at ITER. Integrated commissioning is expected to start in September 2025, with the facility ready for testing by December 2025.The last topic reviewed by STAC members was the plan for neutral beam injection at ITER, including the research plan of the Neutral Beam Test Facility (NBTF). To facilitate their review and to inform STAC members on the latest progress, a group had visited the test facility in Padua, Italy, on 12 May before the start of the STAC-32 meeting at ITER. During in-depth discussions with members of the team there, they learned about the latest successful tests and visited the test stands that are part of the facility. The NBTF Research Plan was formally endorsed by STAC-32.A report from the STAC committee will now be made to the ITER Council, which meets later this month on 18 and 19 June.